System level occupancy counting in a lighting system

ABSTRACT

Disclosed herein is system level occupancy counting in a lighting system configured to obtain an indicator data of a RF spectrum signal (signal) generated by a number of receivers at a number of times in an area. At each respective one of the number of times, for each respective one of the receivers, apply one of a plurality of heurist algorithm heuristic algorithm coefficients to each indicator data of the signal, based on results of the application of the heuristic algorithm coefficients, generate an indicator data metric value for each of the indicator data for the respective time. The lighting system is also configured to process each of the indicator data metric value to compute a plurality of metric values for the respective time and combine the plurality of metric values to compute an output metric value for each of a plurality of probable number of occupants in the area for the respective time. The lighting system is further configured to determine an occupancy count in the area at the respective time based on the computed output metric value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to a patent application entitled “SystemLevel Occupancy Counting in a Lighting System with a Single Transmitterand Multiple Receivers” having an attorney docket number ABLHD-110USfiled herewith and a patent application entitled “System Level OccupancyCounting in a Lighting System with Multiple Transmitters and a SingleReceiver” having an attorney docket number ABLHD-111US also filedherewith.

BACKGROUND

In recent years, a number of systems and methods have been proposed foroccupancy counting within a particular area. Examples of such systemsinclude video sensor monitoring systems, radio frequency identification(RFID) systems, global positioning systems (GPS), and wirelesscommunication systems among others. Some of these systems, such as RFIDand GPS, utilize various radio frequency (RF) based technologies.However, many of these occupancy counting systems have severaldisadvantages. For example, the video sensor monitoring system requiresa considerable number of dedicated sensors that are expensive and thesystem requires a large amount of memory for storing data. The RFIDsystems rely on occupants carrying an RFID tag/card that can be sensedby the RFID system to monitor the occupants. The GPS system usesorbiting satellites to communicate with the terrestrial transceiver todetermine a location of the occupant in the area. However, such systemsare generally less effective indoors or in other environments wheresatellite signals may be blocked, reducing accuracy of detecting theoccupant in the area.

Electrically powered artificial lighting has become ubiquitous in modernsociety. Since the advent of electronic light emitters, such as lightingemitting diodes (LEDs), for general lighting type illuminationapplication, lighting equipment has become increasingly intelligent withincorporation of sensors, programmed controller and networkcommunication capabilities. Automated control, particularly forenterprise installations, may respond to a variety of sensed conditions,such a daylight or ambient light level or occupancy. Commercial gradelighting systems today utilize special purpose sensors and relatedcommunications.

There also have been proposals to detect or count the number ofoccupants in an area based on effects on an RF signal received from atransmitter due to the presence of the occupant(s) in the area. These RFwireless communication systems generally count the number of occupantsin the area based on change in signal characteristics of a data packettransmitted by a single receiver and a single transmitter over thewireless network. However, an inaccurate count of the occupants in thearea can occur when multiple receivers are receiving the RF signals fromone or more transmitters. Further, none of the proposals for detectingor counting the number of occupants provide for the transmission andreceipt of the RF signals in the lighting system.

SUMMARY

The examples disclosed herein improve over RF-based sensing technologiesby counting number of occupants in space.

Some examples disclosed herein include a radio frequency (RF) basedsystem level occupancy counting in a space. In such examples, occupancycount is determined based on measurements of RF perturbations in an areaor space. An example algorithm involves establishing a relationshipbetween the RF perturbations occurring in real time with apre-determined number of occupancy count prior to the real time. Therelationship is compared with the RF perturbations in real time todetermine the occupancy count in the area at real time.

Other examples disclosed herein include heuristically counting number ofoccupants in a space. In such examples, occupancy count is determinedbased on measurements of RF perturbations in an area or space. Anexample machine learning algorithm involves determining optimizedheuristic algorithm heuristic algorithm coefficients associated with theRF perturbations to provide occupancy count in the area at a time. Theoptimized heuristic algorithm heuristic algorithm coefficients areutilized in the example machine learning algorithm to provide theoccupancy count in the area at real time. In one example, prior to thereal time occupancy count, learning occurs to optimize the heuristicalgorithm coefficients, for example, prior to shipping of a product oras part of commissioning. In another example, learning occurs in realtime operation, thus resulting in an on-going learning process tofurther optimize the heuristic algorithm coefficients.

Another example system includes a plurality of lighting elements. Eachof the plurality of lighting elements is a luminaire and includes alight source to illuminate an area. The system also includes a pluralityof wireless communication transmitters for wireless radio frequency (RF)spectrum transmissions in an area, including RF spectrum transmission ata plurality of times. Each of the plurality of transmitters isintegrated into one of the lighting elements. The system also includes aplurality of wireless communication receivers integrated into other ofthe lighting elements. Each of the plurality of wireless receivers isconfigured to receive signals of transmissions from each of thetransmitters through the area at the plurality of times. Each of theplurality of the receivers is configured to generate an indicator dataof a signal characteristic of RF spectrum signal received from each ofthe transmitters at each of the plurality of times. The system alsoincludes a processing circuitry coupled to obtain the indicator data ofRF spectrum signals generated at each of the plurality of times fromeach of the plurality of receivers. At each respective one of theplurality of times, the processing circuitry is to apply a plurality ofheuristic algorithm coefficients to each indicator data of RF spectrumsignal received, from each of the transmitters, in the other of thelighting elements, generated by the respective receiver, for therespective time; based on results of the application of the heuristicalgorithm coefficients to the indicator data, generate an indicator datametric value for each of the indicator data generated by the respectivereceiver for the respective time; and process each of the indicator datametric for each of the indicator data to compute a plurality of metricvalues for the respective receiver for the respective time. Each of theplurality of metric values provide a measurement of each of a pluralityof a probable number of occupants in the area for the respective time.The processing circuitry is also configured to combine the plurality ofmetric values for the respective receiver to compute an output metricvalue for each of the plurality of probable number of occupants in thearea for the respective time; and determine an occupancy count in thearea at the respective time based on the computed output metric valuesfor each of the plurality of probable number of occupants in the area.

An example method includes obtaining, in a lighting system, an indicatordata generated at each of a plurality of times from each of a pluralityof receivers configured to receive radio frequency (RF) spectrum signalsfrom each of a plurality of RF transmitters in an area. The lightingsystem includes a plurality of lighting elements. At each respective oneof the plurality of times in the lighting system, the method alsoincludes applying a plurality of heuristic algorithm coefficients toeach indicator data from each of the plurality of receivers for therespective time, based on results of the application of the heuristicalgorithm coefficients to the indicator data, generating an indicatordata metric value for the indicator data generated by the respectivereceiver for the respective time, processing each of the indicator datametric for each of the indicator data to compute a plurality of metricvalues for the respective receiver for the respective time. Each of theplurality of metric values provide a measurement of each of a pluralityof a probable number of occupants in the area for the respective time.The method further includes combining the plurality of metric values forthe respective receiver to compute an output value for each of theplurality of probable number of occupants in the area for the respectivetime; and determining an occupancy count in the area at the respectivetime based on the computed output metric values for each of theplurality of probable number of occupants in the area.

Another example system includes a plurality of lighting elements. Eachof the plurality of lighting elements is a luminaire and includes alight source to illuminate an area. The system also includes a pluralityof wireless communication transmitters for wireless radio frequency (RF)spectrum transmission in an area, including RF transmission at aplurality of times. Each of the plurality of transmitters is integratedinto one of the lighting elements. The system also includes a pluralityof wireless communication receivers integrated into other of thelighting elements and configured to receive RF spectrum signals oftransmissions from the transmitter through the area at the plurality oftimes. Each of the plurality of the receivers is configured to generatean indicator data of a signal characteristic of received RF spectrumsignal at each of the plurality of times. The system further includes aprocessing circuitry coupled to obtain the indicator data of the RFspectrum signal generated at each of the plurality of times from each ofthe plurality of receivers. The processing circuitry is configured toprocess the indicator data from the plurality of lighting elements todetermine an occupancy count in the area at each of the plurality oftimes.

Additional objects, advantages and novel features of the examples willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing and the accompanying drawings or may be learned by productionor operation of the examples. The objects and advantages of the presentsubject matter may be realized and attained by means of themethodologies, instrumentalities and combinations particularly pointedout in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawing figures depict one or more implementations in accordancewith the present teachings, by way of example only, not by way oflimitation. In the figures, like reference numerals refer to the same orsimilar elements.

FIG. 1A illustrates an example of a wireless topology of a lightingsystem with a single transmitter and multiple receivers.

FIG. 1B illustrates an example of a wireless topology of a lightingsystem with a single receiver and multiple transmitters.

FIG. 2A is a functional block diagram illustrating an example of anoccupancy count system based on the wireless topology of FIG. 1A.

FIG. 2B is a functional block diagram illustrating another example of anoccupancy count system based on the wireless topology of FIG. 1A.

FIG. 2C is a functional block diagram illustrating a further example ofan occupancy count system based on the wireless topology of FIG. 1A.

FIG. 2D is a functional block diagram illustrating another example of anoccupancy count system based on the wireless topology of FIG. 1B.

FIG. 2E is a functional block diagram illustrating another example of anoccupancy count system based on the wireless topology of FIG. 1B.

FIG. 2F is a functional block diagram illustrating further example of anoccupancy count system based on the wireless topology of FIG. 1B.

FIG. 3 illustrates an example of a wireless topology of a lightingsystem with multiple transmitters and multiple receivers.

FIG. 4A is a functional block diagram depicting an example of aheuristic occupancy count system based on the wireless topology of FIG.3.

FIG. 4B is a functional block diagram illustrating another example of aheuristic occupancy count system based on the wireless topology of FIG.3.

FIG. 4C is a functional block diagram illustrating further example of aheuristic occupancy count system based on the wireless topology of FIG.3.

FIG. 5 illustrates an example of a neural network for heuristicallydetermining occupancy count in a lighting system.

FIG. 6A is a high-level flow chart illustration of an example of amethod for a system level determination of an occupancy count.

FIG. 6B is a high-level flow chart illustration of an example of amethod for heuristically determining an occupancy count.

FIG. 7 is a functional block diagram illustrating an example relating toa lighting system of networked devices that provide a variety oflighting capabilities and may implement RF-based occupancy counting.

FIG. 8 is a block diagram of an example of a lighting device thatoperates in and communicates via the lighting system of FIG. 7.

FIG. 9 is a block diagram of an example of a wall switch type userinterface element that operates in and communicates via the lightingsystem of FIG. 7.

FIG. 10 is a block diagram of an example of a sensor type element thatoperates in and communicates via the lighting system of FIG. 7.

FIG. 11 is a block diagram of an example of a plug load controller typeelement that operates in and communicates via the lighting system ofFIG. 7.

FIG. 12 provides a functional block diagram illustration of a host orserver type general purpose computer hardware platform that may beconfigured to implement some or all of the functional processingexamples described with respect to FIGS. 1-6.

FIG. 13 provides functional block diagram illustrations of a userterminal type general purpose computer hardware platforms that may beconfigured to implement some or all of the functional processingexamples described with respect to FIGS. 1-6.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent that the presentteachings may be practiced without such details. In other instances,well known methods, procedures, components, and/or circuitry have beendescribed at a relatively high-level, without detail, in order to avoidunnecessarily obscuring aspects of the present teachings.

Although there have been suggestions to control lighting based on RFwireless counting results, prior RF-based counting systems have notthemselves been integrated as part of a lighting system of which thelighting operation is controlled as a function of the count. Also, priorRF-based counting systems have not themselves been integrated as part ofmachine learning (ML) in a lighting system of which the lightingoperation are controlled as a function of the count.

There is also room for improvement in the RF wireless countingalgorithms for lighting system control. For example, a ML algorithm inthe lighting system may enable a more rapid and real time response sothat an occupant entering a previously empty area perceives that thesystem instantly turns ON the light(s) in the area. As another example,the ML algorithm may offer improved counting accuracy, e.g. to reducefalse positives in determining a number of occupants in the area.

Also, there is a need to manage the area based on the occupant count.other control and manage functions in the area such as heating,ventilation and air conditioning (HVAC), heat mapping, smoke control,equipment control, security control, etc. instead of or in addition tolighting control.

Further, there is room for improvement for accurate determination of anumber of the occupant(s) in a region among multiple different regionsof the area. False positives may occur when detecting number of occupant(s) in a specific region or sub-area when multiple transmitters aretransmitting the RF signals from multiple different regions of the area.For example, a ML algorithm may offer improved occupancy count accuracy,e.g. to reduce false positives in determining the number of occupant(s)in the actual sub-area of interest in the facility.

The examples described below and shown in the drawings integrate RFwireless based occupancy count capabilities in one or more lightingdevices or into lighting devices and/or other elements forming alighting system. Examples of an occupancy count system address some orall of the concerns noted above regarding rapid real time detection ofchanges in occupancy count and/or improved count performance, such asreduction or even elimination of false positive of occupancy count.These advantages and possibly other advantages may be more readilyapparent from the detailed description below and illustration of aspectsof the examples in the drawings.

Referring to FIG. 1A, an example of a wireless topology 101 of alighting element, which includes a single wireless communicationtransmitter (Tx) and a number of wireless communication receivers (Rx)in physical space/area 105. In one implementation, an indoor environmentis described, but it should be readily apparent that the systems andmethods described herein are operable in external environments as well.Specifically, in this example, the area 105 is a room. In oneimplementation, although, not shown, the area 105 may also includecorridors, additional rooms, hallways etc.

As illustrated in the example in FIG. 1A, the area 105 includes threeintelligent system nodes 132, 134, 136. Each such system node has anintelligence capability to transmit a signal or receive a signal andprocess data. In one example, at least one system node includes a lightsource and is configured as a lighting device or luminaire. In anotherexample, a system node includes a user interface component and isconfigured as a lighting controller. In another example a system nodeincludes a switchable power connector and is configured as a plug loadcontroller. In a further example, a system node includes detector aswell as related circuitry and is configured as a lighting relatedsensor.

System node 132 includes a transmitter T1 and system nodes 134 and 136include receivers R1 and R2 respectively. In one implementation,occupancy count in the area 105 is detected according to an occupancycounting procedure as will be described below with respect to FIGS. 2A,2B and 2C below.

In the wireless topology 101, the T1 in the area 105 transmits a RFspectrum (RF) signal for some number (plurality >1) of times. Thetransmission may be specifically for the occupancy count, although thepresent teachings also apply to implementations using the RF wirelesstransmissions for other purposes, such as system network communications(as discussed later regarding other examples). Each of the receiversR1-R2 receives the transmissions of the RF signal through the area 105for each of the plurality of times from T1. Accordingly, each of the R1and R2 is configured to detect a metric of the received RF, which thesystem (e.g. at one or more of the nodes or another processor incommunication with the receivers) uses to determine occupancy countbased on the RF spectrum (RF) signals received from the T1 in the area105.

Referring to FIG. 2A, there is shown a functional block diagram of anexample of an occupancy counting system (system) 200 configured tofunction on a radio frequency (RF) wireless communication network. Asillustrated, the occupancy counting system 200 includes a plurality oflighting elements 202 a-202 n disposed within the physical space/area105 such as a room, corridor, etc. as described above with respect toFIG. 1. In one implementation, an indoor environment is described, butit should be readily apparent that the systems and methods describedherein are operable in external environments as well.

In one implementation, the system 200 includes the three intelligentsystem nodes 132, 134 and 136 as described with respect to FIG. 1Aabove. As discussed above, each such system node has an intelligencecapability to transmit and receive data and process the data. Eachsystem node, for example, may include a receiver (R) and/or atransmitter (T) along with another component used in lightingoperations. In one example, a system node includes a light source and isconfigured as a lighting device. In another example, a system nodeincludes a user interface component and is configured as a lightingcontroller. In another example the system node includes a switchablepower connector and is configured as a plug load controller. In afurther example, a system node includes a detector as well as relatedcircuitry and is configured as a lighting related sensor.

The system node 132 includes a T1, and each of the system nodes 134 and136 includes a R1 and R2 respectively. In the implementation shown inFIG. 2A, each of the system nodes 132, 134 and 136 is integrated in oneof the lighting elements 202 a-202 n such that the system node 132 isintegrated in the lighting element 202 a, the system node 134 isintegrated in the lighting element 202 b and the system node 136 isintegrated in the lighting element 202. Even though the system nodes132, 134 and 136 are shown to be integrated in the lighting elements 202a, 202 b and 202 n respectively, it should be apparent that the systemnodes 132, 134 and 136 are integrated in different lighting devicesamong the lighting devices 202 a-202 n. In another alternateimplementation, two or more of the system nodes 132, 134 and 136 may beintegrated in the same lighting element. For example, the system node132 and the system node 134 may both be incorporated in the lightingelement 202 a; or in another example, the system node 134 and the systemnode 136 may both be incorporated in the lighting element 202 b.

As described above, the Tx is configured to transmit RF signals and eachof the Rx is configured to receive signals from the Tx. In oneimplementation, each of the lighting elements 202 a-202 n includes alight source 206, and is configured as a luminaire, for example, maytake the form of a lamp, light fixture, or other luminaire thatincorporates the light source, where the light source by itself containsno intelligence or communication capability, such as one or more LEDs orthe like, or a lamp (e.g. “regular light bulbs”) of any suitable type.The light source 206 is configured to illuminate some or all of the area105. In one example, each of some number of individual light sources 206is configured to illuminate a portions or a regions of the area 105.Typically, a system will include one or more other system nodes, such asa wall switch, a plug load controller, or a sensor.

In one implementation, the system 200 includes processing circuitry 216coupled to the receivers R1 and R2. In one implementation, theprocessing circuitry is coupled to one or more of the lighting elements202 a-202 n. In an alternate implementation, the processing circuitry216 is coupled to the system 200 via a network (not shown). In oneimplementation, the processing circuitry 216 is implemented in aprocessor executing software or firmware configured to determineoccupancy count in the area 105, although other circuitry orprocessor-based implementations may be used. In one implementation, theprocessing circuitry 216 is implemented in firmware of a processor inthe R1 node and/or in the R2 node.

In one implementation, the system 208 includes a controller 218 coupledto the processing circuitry 216. In one implementation the controller218 may be the same or an additional processor configured to controloperations of elements in the system 200 in response to determination ofoccupancy count in the area 105. For example, in an alternateimplementation, the controller 218 is configured to process a signal tocontrol operation of one or more light sources 206 a-206 n. In onealternate implementation, the controller 218 is configured to turn ONone or more light sources 206 a-206 n upon the occupancy count of one ormore determined by the processing circuitry 216. In one implementation,the controller 218 is configured to turn OFF one or more light sources206 a-206 n upon the occupancy count of zero determined by theprocessing circuitry 216. In another implementation, upon the occupancycount in the area 105, the controller 218 may be configured to provideother control and management functions in the area such as heating,ventilation and air conditioning (HVAC), heat mapping, smoke control,equipment control, security control, etc. instead of or in addition tocontrol of the light source(s). Accordingly, the system 200 isconfigured to function on the RF wireless communications network inaccordance with an implementation of a local control of light source(s)in the lighting element(s), as well as other automation control ofenergy, equipment, operational and management, as discussed above, ofthe area.

In yet another implementation, the controller 218 communicates theoccupancy count to the lighting network via a data packet. The datapacket is received by one or more luminaires in the lighting network,which are configured to turn ON or OFF the light source(s) 206 based onthe occupancy count provided in the data packet. The luminaire oranother node on the lighting network may receive the packet and respondto provide automation of other energy control, equipment control,operational control and management systems (e.g. HVAC, heat mapping,smoke control, equipment control and security control) in the area.Accordingly, the system 200 communicates the occupancy count with othernetworks. In another alternate implementation, the controller 218 iscoupled to the system 200 via a network (not shown). Accordingly, thesystem 200 is configured to function on the RF wireless communicationnetwork in accordance with an implementation of a global control oflight source(s), as well as other automation control of energy,equipment, operational and management, as discussed above, of the areain lighting element(s).

In one implementation, the system nodes typically include a processor,memory and programming (executable instructions in the form of softwareand/or firmware). Although the processor may be a separate circuitry(e.g. a microprocessor), in many cases, it is feasible to utilize thecentral processing unit (CPU) and associated memory of a micro-controlunit (MCU) integrated together with a transceiver in the form of asystem on a chip (SOC). Such an SOC can implement the wirelesscommunication functions as well as the intelligence (e.g. including anyprocessing or controller capabilities) of the system node.

In examples discussed in more detail later, system nodes often mayinclude both a transmitter and a receiver (sometimes referenced togetheras a transceiver), for various purposes. At times, such atransceiver-equipped node may use its transmitter as part of anoccupancy counting operation; and at other times such atransceiver-equipped node may use its receiver as part of an occupancycounting operation. Such transceiver equipped nodes also typicallyinclude a processor, memory and programming (executable instructions inthe form of software and/or firmware). Although the processor may be aseparate circuitry (e.g. a microprocessor), in many cases, it isfeasible to utilize the central processing unit (CPU) and associatedmemory of a micro-control unit (MCU) integrated together with physicalcircuitry of a transceiver in the form of a system on a chip (SOC). Suchan SOC can implement the wireless communication functions as well as theintelligence (e.g. including any processing or controller capabilities)of the system node.

Although the system nodes 132, 134 and 136 of FIG. 2A illustrate animplementation of a single Tx and a single Rx in each of the nodes, thesystem 200 may include other implementations such as multiple Txs in oneor more nodes. Also, FIG. 2A illustrates the implementation of a singleRx in each of the nodes, the system 200 may include otherimplementations such as multiple Rxs in one or more nodes. In theillustrated implementation, the system 200 includes multiple lightingelements 206 a-206 n with either the Tx or the Rx, however, the multiplelighting elements 206 a-206 n of the system 200 may include one or moreTxs and one or more Rxs (see FIG. 7).

For discussion of an initial example of a RF-based occupancy countingoperation, assume that the system 200 includes just the elements shownin FIG. 2A. In one example, each of the system nodes 132, 134 and 136includes the capabilities to communicate over two different RF bands,although the concepts discussed herein are applicable to devices thatcommunicate with luminaires and other system elements via a single RFband. Hence, in the dual band example, the Tx/Rx may be configured forsending and receiving various types of data signals over one band, e.g.for the RF transmission and reception leading to occupancy counting. Theother band may be used or for pairing and commissioning messages overanother band and/or for communications related to detection of RF orhigher level occupancy counting functions, e.g. between receivers R1 andR2 and the controller 220 or the processing circuitry 216. For example,the Tx and Rx are configured as a 900 MHz transmitter and receiver forcommunication of a variety of system or user data, including lightingcontrol data, for example, commands to turn lights ON/OFF, dim up/down,set scene (e.g., a predetermined light setting), and sensor trip events.Alternatively, the Tx and Rx may be configured as a 2.4 GHz transmitterand receiver for Bluetooth low energy (BLE) communication of variousmessages related to commissioning and maintenance of a wireless lightingsystem and/or to communicate results of processing functions in relationto occupant counting.

In one implementation, benefits of the system include the ability totake advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installedin a location in the area 105, and because the system passively monitorssignal broadcasts in the area 105 at a plurality of times, the occupancycounting functionality does not require (does not rely on) the occupantsto carry any device.

At a high level, the T1 transmits a RF spectrum (RF) signal at aplurality of times. The transmission may be specifically for theoccupancy counting. In some cases, however, where the transmitter is inanother lighting device or other lighting system element (e.g. a sensoror a wall switch), the transmissions maybe regular lighting relatedcommunications, such as reporting status, sending commands, reportingsensed events, etc. Each of the R1-R2 receives the transmissions of theRF signal from the T1 through the area 105 during each of the pluralityof times. Each of the R1-R2 generates an indicator data of one or morecharacteristics of the received RF signal at each of the plurality oftimes. Some of examples of the characteristics include but are notlimited to received signal strength indicator (RSSI) data, bit errorrate, packet error rate, phase change etc. or a combination of two ormore thereof. The RSSI data represents measurements of signal strengthof the received RF. The bit error rate is rate of incorrect bits inreceived RF signals versus total number of bits in the transmitted RFsignals. The packet error rate is rate of incorrect packets in receivedRF signals versus total number of packets the transmitted RF signals.Phase change is a change of phase of a received RF signal compared toprevious reception of the RF signal (typically measured between theantennas spaced apart from each other). For the purposes of the presentdescription, we use RSSI data as the characteristics of the RF signalsfor processing by the R1-R2 receivers to generate as the indicator data.Each of the R1-R2 measures the signal strength of the RF signal receivedfrom transmitter T1 and generates the RSSI data based on the signalstrength. The signal strength of each of the RE signal varies over timebased whether an occupant exists or there are a number of occupants(plural) in each path between the T1 and R1 or R2 in the area 105.

For each time, each of the receivers R1-R2 supplies the generatedindicator data of one or more characteristics of the received RF signalto the processing circuitry 216. In one implementation using RSSI as thecharacteristic of interest, the processing circuitry 216 obtains thegenerated RSSI data at each of the plurality of times from the variousreceivers R1-R2 and processes the RSSI data to determine occupancy countin the area 105.

In one implementation, the processing circuitry 216 processes thegenerated RSSI data utilizing a RF signal computation to determineoccupancy count in the area 105 in real time. In one implementation, theRF signal is a line-of-sight (LOS) signal and blocking occurs when atleast one occupant crosses the path between one of the R1 134 and/or theR2 136 and the T1 132 in the area 105 or is located directly (or closeenough) between the one of the R1 134 and/or the R2 136 and the T1 132(i.e., along the so-called line-of-sight path between the receiver andthe transmitter). Similarly, LOS blockage also describes multiplesimultaneous occupant crossings or multiple occupants located on the LOSpath. LOS blocking causes a measurable decrease in the amplitude orstrength of the RF signal received by the R1 134 and/or R2 136 for theduration of time the occupant remains in a blocking position.

In another implementation using RSSI, the received RF signal includes amulti-path signal which is generated based on a multi-path (MP)scattering/fading. In such an implementation, occupant(s) impact thereceive signal as a result of changes they cause in the multi-path (MP)scattering/fading. The MP scattering does not require occupants to belocated along a LOS path. Although MP scattering may not decrease theamplitude/signal strength as dramatically as LOS blocking, MP scatteringresults in a decrease in amplitude/signal strength that is related tothe number of occupants located in the area 105.

In a further implementation, the RF signal reception and associated RSSImeasurements and processing thereof are based on both the LOS blockageand the MP scattering.

In one implementation, the RF signal computation generates theoreticalor mathematical models that provide for a pre-determined number ofoccupants in the area 105 at a time prior to the real time. Thetheoretical model describes RSSI data or a function of the RSSI data ofthe RF signals received from the transmitter by the various receivers asthey relate to various numbers of possible occupants. Theoretical modelsmay take into account one or more ways in which occupants impact thereceived signal. For example, modeling may include mathematical modelingof the LOS blocking as a function of potential number of occupants,mathematical modeling of the multi-path (MP) scattering/fading as afunction of potential number of people, or a combination of both LOS/MPanalysis. In one example, mathematical modeling or LOS, MP or LOS/MP mayinvolve deriving an expression for the probability density function(PDF), probability mass function (PMF), or other statistical orprobabilistic function as they relate to various numbers of occupants.In other examples, mathematical analysis can involve otherspatio-temporal analysis (mathematical analysis of multiple links overspace and time) or other probabilistic spatio-temporal analysis(probabilistic analysis of multiple links over space and time) of theRSSI data. In some examples, the analysis makes use of modeling occupantbehavior within the area. In one example, for instance, the speed oraverage speed of occupants may be assumed and used in the mathematicalmodeling. In another example, any knowledge of spatial preferences orpopular areas can be used in the mathematical modeling. Theoreticalmodels generated are stored, for example in a memory unit (not shown)and subsequently compared to the generated RSSI data for occupancy countin the real time.

In one implementation, the processing circuitry 216 compares thegenerated RSSI data from the plurality of receivers in real time withthresholds or the like produced by the mathematical model. Moreparticularly, in one implementation, the processing circuitry 216analyzes the generated RSSI data to derive a probability densityfunction (PDF) or probability mass function (PMF) representation of thegenerated RSSI data that can be compared with the statistical orprobabilistic functions derived for the theoretical models as describedabove. In one implementation, the processing circuitry 216 determines abest match between the PDF/PMF representation of the generated RSSI datain real time and the theoretical or modeled attributes (or functions ofthe modeled attributes). A variety of methods may be utilized to findthe best match or fit between the function describing the RSSI datagenerated in real time and the function describing the theoretical(modeled) attributes, such as Kullback-Leibler (KL) divergence.

In another implementation, the processing circuitry 216 processes thegenerated RSSI data utilizing a heuristic algorithm to determine anoccupancy count in the area 105. The processing circuitry applies theheuristic algorithm to the indicator data of the RF signals from each ofthe plurality of receivers to compute an output metric value for each ofa plurality of probable number of occupants in the area. The processingcircuitry 216 also compares each one of the plurality of computed outputmetric values with another one of the plurality of computed outputmetric values to identify one of the plurality of computed output metricvalues as having a largest value and indicates that the probable numberof occupants associated with the computed metric value having thelargest value is the current estimate of the occupancy count in the area105. Details of the utilizing of the heuristic algorithm are providedherein below.

In one implementation that takes advantage of the machine learning (ML)capability of the heurist algorithm, the system 200 includes a trusteddetector 230, which provides a known occupancy count value (similar tothe “known answer” as discussed above). Input from the trusted detector230 enables the processor or the like running the heuristic algorithm to“learn” which outcomes are accurate versus outcomes that are notaccurate, so as to improve performance. The trusted detector 230 in theexample may be a standard occupancy sensor, such as passive infraredoccupancy detector, a high resolution camera, low resolution (pixel)camera, a camera based occupancy counting system, ultra-wide band (UWB)radar other radar systems, or sonar systems. Specifically, the trusteddetector 230 provides a known occupancy count value for an accurateoccupancy count in the area 105 at each of the multiple times. In oneimplementation, the known occupancy count value is pre-determined priorto heuristically determining the occupancy count in the area 105.

In one implementation, the processing circuitry 216 obtains theindicator data of the RF signal generated for multiple times (ta-tn)from each of the R1 and R2. The processing circuitry 216 applies one ofa heuristic algorithm heuristic algorithm coefficient heuristicalgorithm coefficient among a set of heuristic algorithm heuristicalgorithm coefficients to each of the indicator data from each of the R1and R2 to generate an indicator data metric value for each of theindicator data from each of the R1 and R2 for the times ta-tn. Eachheuristic algorithm heuristic algorithm coefficient among the set ofheuristic algorithm heuristic algorithm coefficients may be randomlyselected at an initial stage of training. In one implementation, aheuristic algorithm heuristic algorithm coefficient is a variable. Inone implementation, a value of the heuristic algorithm heuristicalgorithm coefficient applied to an indicator data from R1 is the sameas the value of the heuristic algorithm heuristic algorithm coefficientapplied to another indicator data that is from R2. In anotherimplementation, a value of a first heuristic algorithm heuristicalgorithm coefficient applied to an indicator data from the R1 isdifferent from value of another (second) heuristic algorithm heuristicalgorithm coefficient applied to another indicator data from R2. In oneimplementation, the processing circuitry 216 processes the indicatordata metric values to compute metric values associated with each of theR1 and R2 at each of the times ta-tn. The metric values provide ameasurement of each of a plurality of a probable or possible number ofoccupants in the area for each of the times ta-tn. Some examples of themeasurements may include but not limited to percentage, decimal, ratio,rates etc. In one implementation, the processing circuitry 216combines/adds the metric values associated with R1 with metric valuesassociated with R2 to compute an output metric value for each of theplurality of probable number of occupants in the area for each of thetimes ta-tn. In one implementation, the processing circuitry 216compares each of the plurality of output metric values with one anotherto determine which of the plurality of output metric values has thehighest value. The processing circuitry 216 determines that the probablenumber of occupants in the area with the largest output metric value asthe occupancy count in the area for each of the times ta-tn. In oneimplementation, the processing circuitry 216 compares the occupancycount in the area with the known occupancy count value generated by thetrusted detector for each of the ta-tn. Specifically, the processingcircuitry 216 compares the occupancy count in the area at each of theta-tn with the known occupancy count value, for example, an output ofthe trusted detector 230, to determine an accurate occupancy count inthe area as described in greater detail below. In one implementation,the system 202 includes a learning module 220 coupled to the processingcircuitry 216 to determine whether the set of heuristic algorithmcoefficients are optimized heuristic algorithm coefficients based on thecomparison by the processing circuitry 216 at the times ta-tn to detectan accurate occupancy count in the area. In one implementation, upondetermination, that the set of heuristic algorithm coefficients areoptimized heuristic algorithm coefficients, the learning module 220instructs the processing circuitry 216 to utilize the optimizedheuristic algorithm coefficients in real time. In one implementation,upon determination, that the set of heuristic algorithm coefficients arenot optimized heuristic algorithm coefficients, the learning module 220instructs the processing circuitry 216 to update one or more heuristicalgorithm coefficients among the set of heuristic algorithm coefficientsand utilize the updated one or more heuristic algorithm coefficients ina next time. The above implementations are described in greater detailbelow.

In one implementation, the processing circuitry 216 determines that theoccupancy count at a time t1 among the times ta-tn is same as the knownoccupancy count value. In one implementation, the learning module 220determines, that the set of heuristic algorithm coefficients aredetermined to be optimized heuristic algorithm coefficients to beapplied to the indicator data for the time t1 to determine the accurateoccupancy count. In one implementation, the learning module 220instructs the processing circuitry 216 to utilize the optimizedheuristic algorithm coefficients to apply to each indicator data amongthe plurality of indicator data from each of the plurality of receiversfor the time t1 to detect the occupancy count in real time. Accordingly,the processing circuitry 216 applies the optimized heuristic algorithmcoefficients to determine the occupancy count in real time.

In another implementation, the processing circuitry 216 determines thatthe occupancy count at a time t1 among the times ta-tn is different thanthe known occupancy count value. The learning module 220 determines thatthe set of heuristic algorithm coefficients are not optimized heuristicalgorithm coefficients and thus updates the one or more heuristicalgorithm coefficients among the set of the heuristic algorithmcoefficients to generate updated set of heuristic algorithmcoefficients. The learning module 220 instructs the processing circuitry216 to utilize the updated set of heuristic algorithm coefficients in anext time. The processing circuitry 216 applies the updated heuristicalgorithm coefficients to corresponding indicator data from each of theR1 and R2 to generate an updated indicator data metric value for each ofthe indicator data from each of the R1 and R2 at the time t1. In oneimplementation, the processing circuitry 216 processes each of theupdated indicator data metric values to compute updated occupancy countat t1. In one implementation, the processing circuitry 216 determinesthat the updated occupancy count at the time t1 is the same as the knownoccupancy count value. As such, the learning module 220 determines thatthe updated set of heuristic algorithm coefficients are optimizedheuristic algorithm coefficients to be applied to the indicator data forthe time t1 to determine the accurate occupancy count in real time. Inanother implementation, the processing circuitry 216 determines that theupdated occupancy count is different than the known occupancy countvalue. The processing circuitry 216 and the learning module 220 repeatsthe above process for t1 until the occupancy count is same as the knownoccupancy count value to determine that the set of heuristic algorithmcoefficients corresponding to the indicator data from each of the R1 andR2 are the optimized heuristic algorithm coefficients for the t1 amongthe ta-tn to accurately determine the occupancy count at real time.Accordingly, the processing circuitry 216 applies the optimizedheuristic algorithm coefficients to determine the occupancy count inreal time.

In one implementation, the occupancy count is determined for each of theindicator data at each of the ta-tn and compared with the knownoccupancy count value to determine the optimized heuristic algorithmcoefficients for each of the ta-tn to detect an accurate occupancy countin the area 105 of FIG. 1A at each of the ta-tn. In one implementation,the optimized set of heuristic algorithm coefficients for each of theta-tn are utilized by the processing circuitry 216 to detect accurateoccupancy count in the area 105 of FIG. 1A at real time.

Referring to FIG. 2B, there is shown a functional block diagram of anexample of an occupancy counting system (system) 201 configured tofunction on a radio frequency (RF) wireless communication network. Inone implementation, the system 201 is similar to the system 200 of FIG.2A except the processing circuitry 216 is coupled to the one or more ofthe lighting elements via a network 240. In one implementation, thenetwork 240 is a wireless communication network. In one example, thenetwork 240 is a BLE mesh. In one implementation, the network 240 is awired network. In one implementation, the processing circuitry 216 is acloud computing system which includes a plurality of processingservers/machines, which work together or independently to process theindicator data to determine the occupancy count in the area 105. In analternate implementation, the controller 218 is coupled to theprocessing circuitry 216 via the network 240. In such alternateimplementation, the controller 218 is a cloud computing system whichincludes a plurality of processing servers/machines, which work togetheror independently to control operations of one or more elements (e.g.light source 206 a-206 n) of the lighting elements 202 a-202 n and/orprovide automation of other energy control, equipment control,operational control and management systems (e.g. HVAC, heat mapping,smoke control, equipment control and security control) in the area basedon determination of the occupancy count by the processing circuitry 216.Accordingly, implementation of the system 201 is configured to globallycontrol the light source(s) of the lighting element(s), as well as otherautomation control of energy, equipment, operational and management, asdiscussed above, of the area in the lighting system.

Referring to FIG. 2C, there is shown a functional block diagram of anexample of an occupancy counting system (system) 202 configured tofunction on a radio frequency (RF) wireless communication network. Inone implementation, the system 202 is similar to the system 200 of FIG.2A except the processing circuitry 216 is integrated in a lightingelement 202. The processing circuitry 216 functions to process theindicator data to determine the occupancy count in the area 105 asdiscussed in detail above. The system 202 also includes plurality oflighting elements 202 a-202 n including the lighting element 202.Although, not illustrated, in an alternate implementation, theprocessing circuitry 216 is integrated in one of the plurality oflighting elements 202 a-202 n. Each of the plurality of lightingelements includes a corresponding light source among a plurality oflight sources 202 a-202 n.

Referring to FIG. 1B, an example of a wireless topology 103 of alighting system includes a single wireless communication receiver (Rx)and a number of wireless communication transmitters (TXs) in physicalspace/area 105. In one implementation, an indoor environment isdescribed, but it should be readily apparent that the systems andmethods described herein are operable in external environments as well.Specifically, in this example, the area 105 is a room. In oneimplementation, although, not shown, the area 105 may also includecorridors, additional rooms, hallways etc. As illustrated in the examplein FIG. 1B, the area 105 includes three intelligent system nodes, out ofwhich two are Tx 132, and Tx 133, and one is the Rx 136. As discussedabove, each such system node has an intelligence capability to transmita signal or receive a signal and process data. In one example, at leastone system node includes a light source and is configured as a lightingdevice or luminaire. In another example, a system node includes a userinterface component and is configured as a lighting controller. Inanother example a system node includes a switchable power connector andis configured as a plug load controller. In a further example, a systemnode includes detector as well as related circuitry and is configured asa lighting related sensor. In one implementation, occupancy count in thearea 105 is detected according to an occupancy counting procedure aswill be described below with respect to FIGS. 2D, 2E and 2F below.

In the wireless topology 103, the T1 and T2 in the area 105 transmits aRF spectrum (RF) signal for some number (plurality >1) of times. Thetransmission may be specifically for the occupancy count, although thepresent teachings also apply to implementations using the RF wirelesstransmissions for other purposes, such as system network communications(as discussed later regarding other examples). The R1 receives thetransmissions of the RF signals through the area 105 for each of theplurality of times from T1 and T2. Accordingly, the R1 is configured todetect a metric of the received RF, which the system (e.g. at one ormore of the nodes or another processor in communication with thereceivers) uses to determine occupancy count based on the RF signalsreceived from the T1 and the T2 in the area 105.

Referring to FIG. 2D, there is shown a functional block diagram of anexample of an occupancy counting system (system) 250 configured tofunction on a radio frequency (RF) wireless communication network. Asillustrated, the occupancy counting system 250 includes a plurality oflighting elements 202 a-202 n disposed within the physical space/area105 such as a room, corridor, etc. as described above with respect toFIG. 1B. In one implementation, an indoor environment is described, butit should be readily apparent that the systems and methods describedherein are operable in external environments as well.

In one implementation, the system 250 includes the three intelligentsystem nodes, as described with respect to FIG. 1B above. As discussedabove, each such system node has an intelligence capability to transmitand receive data and process the data. Each system node, for example,may include a receiver (R) and/or a transmitter (T) along with anothercomponent used in lighting operations. In one example, a system nodeincludes a light source and is configured as a lighting device. Inanother example, a system node includes a user interface component andis configured as a lighting controller. In another example the systemnode includes a switchable power connector and is configured as a plugload controller. In a further example, a system node includes a detectoras well as related circuitry and is configured as a lighting relatedsensor. The system node 134 includes a R1, and each of the system nodes132 and 133 includes a T1 or T2 respectively. In the implementationshown in FIG. 2D, each of the system nodes 132, 134 and 136 isintegrated in one of the lighting elements 202 a-202 n. Even though, thesystem nodes 132, 134 and 136 are shown to be integrated in the lightingelements 202 a, 202 b and 202 n respectively, it should be apparent thatthe system nodes 132, 134 and 136 are integrated in different lightingelements among the lighting elements 202 a-202 n. In another alternateimplementation, two or more of the system nodes 132, 134 and 136 may beintegrated in the same lighting element. For example, the system node132 and the system node 134 may both be incorporated in the lightingelement 202 a or in another example, the system node 134 and the systemnode 136 may both be incorporated in the lighting element 202 b.

As described above, each of the Tx is configured to transmit RF spectrum(RF) signals and the Rx is configured to receive signals from each ofthe Tx. Similar to the system 200 in FIG. 2A, each of the lightingelements 202 a-202 n in the system 250 of FIG. 2D also includes a lightsource 206, and is configured as a luminaire, for example, may take theform of a lamp, light fixture, or other luminaire that incorporates thelight source, where the light source by itself contains no intelligenceor communication capability, such as one or more LEDs or the like, or alamp (e.g. “regular light bulbs”) of any suitable type. The light source206 is configured to illuminate some or all of the area 105. In oneexample, each of some number of individual light sources 206 isconfigured to illuminate a portions or a regions of the area 105.Typically, a system will include one or more other system nodes, such asa wall switch, a plug load controller, or a sensor.

The system 250 also includes processing circuitry 216. The processingcircuitry 216 is coupled to the receivers R1. In one implementation, theprocessing circuitry is coupled to one or more of the lighting elements202 a-202 n. In an alternate implementation, the processing circuitry216 is coupled to the system 200 via a network (not shown). In oneimplementation, the processing circuitry 216 is implemented in aprocessor executing software or firmware configured to determineoccupancy count in the area 105, although other circuitry orprocessor-based implementations may be used. Similar to the system 200in FIG. 2A, the system 250 in FIG. 2D also includes a controller 218coupled to the processing circuitry 216. In one implementation thecontroller 218 may be the same or an additional processor configured tocontrol operations of elements in the system 250 in response todetermination of occupancy count in the area 105. For example, in analternate implementation, the controller 218 is configured to process asignal to control operation of one or more light sources 206 a-206 n. Inone alternate implementation, the controller 218 is configured to turnON one or more light sources 206 a-206 n upon the occupancy count of oneor more determined by the processing circuitry 216. In oneimplementation, the controller 218 is configured to turn OFF one or morelight sources 206 a-206 n upon the occupancy count of zero determined bythe processing circuitry 216. In another implementation, the controller218 is configured to provide automation of other energy control,equipment control, operational control and management systems (e.g.HVAC, heat mapping, smoke control, equipment control and securitycontrol) in the area upon the occupancy count determined by theprocessing circuitry 216. Accordingly, the system 250 is configured tofunction on the RF wireless communications network in accordance with animplementation of a local control of light source(s) in the lightingelement(s), as well as other automation control of energy, equipment,operational and management, as discussed above, of the area in thelighting system.

In examples discussed in more detail later, system nodes often mayinclude both a transmitter and a receiver (sometimes referenced togetheras a transceiver), for various purposes. At times, such atransceiver-equipped node may use its transmitter as part of anoccupancy counting operation; and at other times such atransceiver-equipped node may use its receiver as part of an occupancycounting operation.

Although, FIG. 2D illustrates the implementation of a single Rx and asingle Tx in each of the nodes, the system 203 may include otherimplementations such as multiple Rx in one or more nodes and multiple Txin one or more nodes. Further, the system 203 may include one or more Txand one or more Rx in each of the nodes. In the illustratedimplementation, the system 250 includes multiple lighting elements 206a-206 n with either the Tx or the Rx, however, the multiple lightingelements 206 a-206 n of the system 200 may include one or more Tx andone or more Rx (see FIG. 7).

For discussion of an initial example of a RF-based occupancy countingoperation, assume that the system 250 includes just the elements shownin FIG. 2D. In one example, each of the system nodes 132, 134 and 136includes the capabilities to communicate over two different RF bands,although the concepts discussed herein are applicable to devices thatcommunicate with luminaires and other system elements via a single RFband. Hence, in the dual band example, the Tx/Rx may be configured forsending and receiving various types of data signals over one band, e.g.for the RF transmission and reception leading to occupancy counting. Theother band may be used or for pairing and commissioning messages overanother band and/or for communications related to detection of RF orhigher level occupancy counting functions, e.g. between receivers R1 andR2 and the controller 218 or the processing circuitry 216. For example,the Tx and Rx are configured as a 900 MHz transmitter and receiver forcommunication of a variety of system or user data, including lightingcontrol data, for example, commands to turn lights ON/OFF, dim up/down,set scene (e.g., a predetermined light setting), and sensor trip events.Alternatively, the Tx and Rx may be configured as a 2.4 GHz transmitterand receiver for Bluetooth low energy (BLE) communication of variousmessages related to commissioning and maintenance of a wireless lightingsystem and/or to communicate results of processing functions in relationto occupant counting.

In one implementation, benefits of the system include the ability totake advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installedin a location in the area 105, and because the system passively monitorssignal broadcasts in the area 105 at a plurality of times, the occupancycounting functionality does not require (does not rely on) the occupantsto carry any device.

At a high level, each of the T1 and T2 transmits a RF spectrum (RF)signal at each of a plurality of times. The transmission may bespecifically for the occupancy counting. In some cases, however, wherethe transmitter is in another lighting device or other lighting systemelement (e.g. a sensor or a wall switch), the transmissions mayberegular lighting related communications, such as reporting status,sending commands, reporting sensed events, etc. The R1 receives thetransmissions of the RF signals from the T1 and the T2 through the area105 during each of the plurality of times. The R1 generates an indicatordata of one or more characteristics of the received RF signals at theplurality of times. Some of examples of the characteristics include butare not limited to received signal strength indicator (RSSI) data, biterror rate, packet error rate, phase change etc. or a combination of twoor more thereof. The RSSI data represents measurements of signalstrength of the received RF. The bit error rate is rate of incorrectbits in received RF signals versus total number of bits in thetransmitted RF signals. The packet error rate is rate of incorrectpackets in received RF signals versus total number of packets thetransmitted RF signals. Phase change is a change of phase of a receivedRF signal compared to previous reception of the RF signal (typicallymeasured between the antennas spaced apart from each other). For thepurposes of the present description, we use RSSI data as thecharacteristics of the RF signal for processing by the R1 to generate asthe indicator data. The R1 measures the signal strength of the receivedRF signals transmitted by T1 and T2; and generate the RSSI data based onthe signal strength of the RF signals transmitted by T1 and T2. Thesignal strength of each of the RF signal varies over time based whetheran occupant exists or there are a number of occupants in each a path theT1 and R1 and/or the T2 and R1 in the area 105.

For each time, the R1 supplies the generated indicator data of one ormore characteristics of the received RF signal to the processingcircuitry 216. In one implementation using RSSI as the characteristic ofinterest, the processing circuitry 216 obtains the generated RSSI dataat each of the plurality of times from the R1 and processes the RSSIdata to determine occupancy count in the area 105.

In one implementation, the processing circuitry 216 processes thegenerated RSSI data utilizing a RF signal computation to determineoccupancy count in the area 105 in real time. In one implementation, theRF signal is a line-of-sight (LOS) signal blocking occurs when at leastone occupant crosses the path between one of the T1 132 and/or the T2133 and R1 134 in the area 105 or is located directly (or close enough)between the one of the T1 132 and/or the T2 133 and the R1 134 (i.e.,along the so-called line-of-sight path between the receiver and thetransmitter). Similarly, LOS blockage also describes multiplesimultaneous occupant crossings or multiple occupants located on the LOSpath. LOS blocking causes a measurable decrease in the amplitude orstrength of the RF signal received by the R1 134 for the duration oftime the occupant remains in a blocking position. In anotherimplementation, using RSSI, the received RF signal includes a multi-pathsignal which is generated based on a multi-path (MP) scattering/fading.In such implementation, occupant(s) impact the receive signal as aresult of changes they cause in the multi-path (MP) scattering/fading.The MP scattering does not require occupants to be located along a LOSpath. Although MP scattering may not decrease the amplitude/signalstrength as dramatically as LOS blocking, MP scattering results in adecrease in amplitude/signal strength that is related to the number ofoccupants located in the area 105. In a further implementation, the RFsignal reception and associated RSSI measurements and processing thereofare based on both the LOS blockage and the MP scattering.

As discussed above, in one implementation, the RF signal computationgenerates theoretical or mathematical models that provide for apre-determined number of occupants in the area 105 at a time prior tothe real time. The theoretical model describes RSSI data or a functionof the RSSI data of the RF signals received from the transmitter by thevarious receivers as they relate to various numbers of possibleoccupants. Theoretical models may take into account one or more ways inwhich occupants impact the received signal. For example, modeling mayinclude mathematical modeling of the LOS blocking as a function ofpotential number of occupants, mathematical modeling of the multi-path(MP) scattering/fading as a function of potential number of people, or acombination of both LOS/MP analysis. In one example, mathematicalmodeling or LOS, MP or LOS/MP may involve deriving an expression for theprobability density function (PDF), probability mass function (PMF), orother statistical or probabilistic function as they relate to variousnumbers of occupants. In other examples, mathematical analysis caninvolve other spatio-temporal analysis (mathematical analysis ofmultiple links over space and time) or other probabilisticspatio-temporal analysis (probabilistic analysis of multiple links overspace and time) of the RSSI data. In some examples, the analysis makesuse of modeling occupant behavior within the area. In one example, forinstance, the speed or average speed of occupants may be assumed andused in the mathematical modeling. In another example, any knowledge ofspatial preferences or popular areas can be used in the mathematicalmodeling. Theoretical models generated are stored, for example in amemory unit (not shown) and subsequently compared to the generated RSSIdata for occupancy count in the real time.

As discussed above, in one implementation, the processing circuitry 216compares the generated RSSI data from the plurality of receivers in realtime with thresholds or the like produced by the mathematical model.More particularly, in one implementation, the processing circuitry 216analyzes the generated RSSI data to derive a probability densityfunction (PDF) or probability mass function (PMF) representation of thegenerated RSSI data that can be compared with the statistical orprobabilistic functions derived for the theoretical models as describedabove. In one implementation, the processing circuitry 216 determines abest match between the PDF/PMF representation of the generated RSSI datain real time and the theoretical or modeled attributes (or functions ofthe modeled attributes). A variety of methods may be utilized to findthe best match or fit between the function describing the RSSI datagenerated in real time and the function describing the theoretical(modeled) attributes, such as Kullback-Leibler (KL) divergence.

As discussed above, in another implementation, the processing circuitry216 processes the generated RSSI data utilizing a heuristic algorithm todetermines an occupancy count in the area 105. The processing circuitryapplies the heuristic algorithm to the indicator data of the RF signalsfrom each of the plurality of receivers to compute an output metricvalue for each of a plurality of probable number of occupants in thearea. The processing circuitry 216 also compares each one of theplurality of computed output metric values with another one of theplurality of computed output metric values to identify one of theplurality of computed output metric values as having a largest valueindicates that the probable number of occupants associated with thecomputed metric value having the larges value is the current estimate ofthe occupancy count in the area 105. Details of the utilizing of theheuristic algorithm are provided herein below.

As discussed above, in one implementation that takes advantage of themachine learning (ML) capability of the heurist algorithm, the system250 includes a trusted detector 230, which provides a known occupancycount value (similar to the “known answer” as discussed above). Inputfrom the trusted detector 230 enables the processor or the like runningthe heuristic algorithm to “learn” which outcomes are accurate versusoutcomes that are not accurate, so as to improve performance. Thetrusted detector 230 in the example may be a standard occupancy sensor,such as passive infrared occupancy detector or a camera based occupancycounting system. Specifically, the trusted detector 230 provides a knownoccupancy count value for an accurate occupancy count in the area 105 ateach of the multiple times. In one implementation, the known occupancycount value is pre-determined prior to heuristically determining theoccupancy count in the area 105.

In one implementation, the processing circuitry 216 obtains theindicator data of the RF signal (transmitted by T1 and T2) generated formultiple times (ta-tn) from the R1. The processing circuitry 216 appliesone of a heuristic algorithm coefficient among a set of heuristicalgorithm heuristic algorithm coefficients to each of the indicator datafrom the R1 to generate an indicator data metric value for each of theindicator data from the R1 and R2 for the times to-tn. Each heuristicalgorithm coefficient among the set of heuristic algorithm coefficientsmay be randomly selected at an initial stage of training. In oneimplementation, a heuristic algorithm coefficient is a variable. In oneimplementation, the processing circuitry 216 processes the indicatordata metric values to compute metric values associated with the R1 ateach of the times ta-tn. The metric values provide a measurement of eachof a plurality of a probable or possible number of occupants in the areafor each of the times ta-tn. Some examples of the measurements mayinclude but not limited to percentage, decimal, ratio, rates etc. In oneimplementation, the processing circuitry 216 combines/adds the metricvalues associated with R1 to compute an output metric value for each ofthe plurality of probable number of occupants in the area for each ofthe times ta-tn. In one implementation, the processing circuitry 216compares each of the plurality of output metric values with one anotherto determine which of the plurality of output metric values has thehighest value. The processing circuitry 216 determines that the probablenumber of occupants in the area with the largest output metric value asthe occupancy count in the area for each of the times ta-tn. In oneimplementation, the processing circuitry 216 compares the occupancycount in the area with the known occupancy count value generated by thetrusted detector for each of the ta-tn. Specifically, the processingcircuitry 216 compares the occupancy count in the area at each of theta-tn with the known occupancy count value, for example, an output ofthe trusted detector 230, to determine an accurate occupancy count inthe area as described in greater detail below. In in one implementation,the system 250 includes a learning module 220 coupled to the processingcircuitry 216 to determine whether the set of heuristic algorithmcoefficients are optimized heuristic algorithm coefficients based on thecomparison by the processing circuitry 216 at the times ta-tn to detectan accurate occupancy count in the area. In one implementation, upondetermination, that the set of heuristic algorithm coefficients areoptimized heuristic algorithm coefficients, the learning module 220instructs the processing circuitry 216 to utilize the optimizedheuristic algorithm coefficients in real time. In one implementation,upon determination, that the set of heuristic algorithm coefficients arenot optimized heuristic algorithm coefficients, the learning module 220instructs the processing circuitry 216 to update one or more heuristicalgorithm coefficients among the set of heuristic algorithm coefficientsand utilize the updated one or more heuristic algorithm coefficients ina next time. The above implementations are described in greater detailbelow.

As discussed above, in one implementation, the processing circuitry 216determines that the occupancy count at a time t1 among the times ta-tnis same as the known occupancy count value. In one implementation, thelearning module 220 determines, that the set of heuristic algorithmcoefficients are determined to be optimized heuristic algorithmcoefficients to be applied to the indicator data for the time t1 todetermine the accurate occupancy count. In one implementation, thelearning module 220 instructs the processing circuitry 216 to utilizethe optimized heuristic algorithm coefficients to apply the indicatordata from the receiver for the time t1 to detect the occupancy count inreal time. Accordingly, the processing circuitry 216 applies theoptimized heuristic algorithm coefficients to determine the occupancycount in real time.

As discussed above, in another implementation, the processing circuitry216 determines that the occupancy count at a time t1 among the timesta-tn is different than the known occupancy count value. The learningmodule 220 determines that the set of heuristic algorithm coefficientsare not optimized heuristic algorithm coefficients and thus updates theone or more heuristic algorithm coefficients among the set of theheuristic algorithm coefficients to generate updated set of heuristicalgorithm coefficients. The learning module 220 instructs the processingcircuitry 216 to utilize the updated set of heuristic algorithmcoefficients in a next time. The processing circuitry 216 applies theupdated heuristic algorithm coefficients to corresponding indicator datafrom the R1 to generate an updated indicator data metric value for theindicator data from the R1 at the time t1. In one implementation, theprocessing circuitry 216 processes the updated indicator data metricvalues to compute updated occupancy count at t1. In one implementation,the processing circuitry 216 determines that the updated occupancy countat the time t1 is the same as the known occupancy count value. As such,the learning module 220 determines that the updated set of heuristicalgorithm coefficients are optimized heuristic algorithm coefficients tobe applied to the indicator data for the time t1 to determine theaccurate occupancy count in real time. In another implementation, theprocessing circuitry 216 determines that the updated occupancy count isdifferent than the known occupancy count value. The processing circuitry216 and the learning module 220 repeats the above process for t1 untilthe occupancy count is same as the known occupancy count value todetermine that the set of heuristic algorithm coefficients correspondingto the indicator data from the R1 are the optimized heuristic algorithmcoefficients for the t1 among the ta-tn to accurately determine theoccupancy count at real time. Accordingly, the processing circuitry 216applies the optimized heuristic algorithm coefficients to determine theoccupancy count in real time.

In one implementation, the occupancy count is determined for theindicator data at each of the ta-tn and compared with the knownoccupancy count value to determine the optimized heuristic algorithmcoefficients for each of the ta-tn to detect an accurate occupancy countin the area 105 of FIG. 1B at each of the ta-tn. In one implementation,the optimized set of heuristic algorithm coefficients for each of theta-tn are utilized by the processing circuitry 216 to detect accurateoccupancy count in the area 105 of FIG. 1B at real time.

Referring to FIG. 2E, there is shown a functional block diagram of anexample of an occupancy counting system (system) 251 configured tofunction on a radio frequency (RF) wireless communication network. Inone implementation, the system 251 is similar to the system 250 of FIG.2D except the processing circuitry 216 is coupled to the one or more ofthe lighting elements via a network 240. In one implementation, thenetwork 240 is a wireless communication network. In one example, thenetwork 240 is a BLE mesh. In one implementation, the network 240 is awired network. In one implementation, the processing circuitry 216 is acloud computing system which includes a plurality of processingservers/machines, which work together or independently to process theindicator data to determine the occupancy count in the area 105. In analternate implementation, the controller 218 is coupled to theprocessing circuitry 216 via the network 240. In such alternateimplementation, the controller 218 is a cloud computing system whichincludes a plurality of processing servers/machines, which work togetheror independently to control operations of one or more elements (e.g.light source 206 a-206 n) of the lighting elements 202 a-202 n, and toprovide automation of other energy control, equipment control,operational control and management systems (e.g. HVAC, heat mapping,smoke control, equipment control and security control) in the area uponthe occupancy count determined by the processing circuitry 216 as wellas other automation control of energy, equipment, operational andmanagement, as discussed above, of the area based on determination ofthe occupancy count by the processing circuitry 216. Accordingly,implementation of the system 251 is configured to globally control thelight source(s) of the lighting element(s), as well as other automationcontrol of energy, equipment, operational and management, as discussedabove, of the area.

Referring to FIG. 2F, there is shown a functional block diagram of anexample of an occupancy counting system (system) 252 configured tofunction on a radio frequency (RF) wireless communication network. Inone implementation, the system 252 is similar to the system 250 of FIG.2D except the processing circuitry 216 is integrated in a lightingelement 202. The processing circuitry 216 functions to process theindicator data to determine the occupancy count in the area 105 asdiscussed in detail above. The system 252 also includes plurality oflighting elements 202 a-202 n including the lighting element 202.Although, not illustrated, in an alternate implementation, theprocessing circuitry 216 is integrated in one of the plurality oflighting elements 202 a-202 n. Each of the plurality of lightingelements includes a corresponding light source among a plurality oflight sources 202 a-202 n.

Referring to FIG. 3, an example of a wireless topology 301 of a lightingsystem includes a number of wireless communication transmitters (Tx) anda number of wireless communication receiver (Rx) in physical space/area305. In one implementation, indoor environment is described, but itshould be readily apparent that the systems and methods described hereinare operable in external environments as well. Specifically, in thisexample, the area 305 includes a combination of a room 360, and ahallway 380. In one implementation, although, not shown, the area 305may also include corridors, additional rooms, additional hallways etc.In one implementation, although, not shown, the area 305 may alsoinclude corridors, additional rooms, hallways etc.

A wall 390 separates the room 360 from the hallway 380 with an opening392. As illustrated in the example in FIG. 3, the area 305 includes sixintelligent system nodes 332, 334, 336, 338, 340, and 342. Each suchsystem node has an intelligence capability to transmit a signal orreceive a signal and process data. In one example, at least one systemnode includes a light source and is configured as a lighting device orluminaire. In another example, a system node includes a user interfacecomponent and is configured as a lighting controller. In another examplea system node includes a switchable power connector and is configured asa plug load controller. In a further example a system node includesdetector as well as related circuitry and is configured as a lightingrelated sensor.

In general, a heuristic algorithm with prior or ongoing training formachine learning, “learns” how to manipulate various inputs, possiblyincluding previously generated outputs, in order to generate current newoutputs. As part of this learning process, the algorithm receivesfeedback on prior outputs and possibly some other inputs. Then, themachine learning algorithm calculates parameters to be associated withthe various inputs (e.g. the previous outputs, feedback, etc.). Theparameters are then utilized by the machine learning to manipulate theinputs and generate the current outputs intended to improve some aspectof system performance in a desired manner. During the machine learningphase, the training data is the discrepancy between the outputs of apresent system and the outputs of a trusted system.

In a lighting system with occupancy count, for example, the trainingdata may be the discrepancy between the outputs of an RF based detectionsystem operating in a user/consumer installation and a trusted occupancycount system such as a standard occupancy sensor (e.g. such as a sensorusing passive infrared (PIR) of or a camera based system). Machinelearning techniques such as artificial neural network is applied toreduce the discrepancy, for example, by optimizing one or more heuristicalgorithm coefficients used in the real time occupancy count. Trainingcan take place ahead of the time (before product shipment/commissioning)or in the field as an on-going optimization to reduce false positives incounting occupant(s).

An example may apply a “supervised learning” approach in which thesystem will be provided a “known answer” from a “trusted detector” andmachine learning is used to optimize the occupancy count algorithm tominimize the difference between the system output and the “knownanswer.” A trusted detector may be a passive infrared occupancy detectoror a camera. The particular machine learning approach can be one ofdecision tree or artificial neural net.

Learning can take place prior to shipping product or as part ofcommissioning after installation. In either such case, the system maynormally operate in the field without using the trusted detector in realtime.

Alternatively, a trusted detector can be installed with the system inthe field and utilized in real time, in which case, there may beon-going machine learning. For an ongoing learning implementation, thedata can be routed to a cloud, learning can take place on anothersystem, and then the improved algorithm (e.g. in the form of new nodeparameters in the case of a neural network) can be downloaded to theinstalled lighting system.

In one implementation, details of the heuristic algorithm and machinelearning are provided in more detail with respect to the example of FIG.3.

System nodes 332, 334, 136 and 138 are located in the room 360 and thesystem nodes 340 and 342 are located in the hallway 380. Each of thesystem nodes 332, 334 and 336 include transmitters T1, T2 and T3respectively and each of the system nodes 338, 340 and 342 includereceivers R1, R2 and R3 respectively. In one implementation, theoccupancy count in the entire area 305 and/or a region (for example room360) in the area 305 is detected according to a ML occupancy countingprocedure as will be described below with respect to FIGS. 4A, 4B and4C.

In the wireless topology 301 each of the transmitters T1-T3 in the area305 transmits a RF spectrum (RF) signal for some number (plurality >1)of times. The transmissions from T1-T3 may be specifically for theoccupancy count, although the present teachings also apply toimplementations using the RF wireless transmissions for other purposes,such as system network communications (as discussed later regardingother examples. Each of the receivers R1-R3 in the area 305 receives thetransmissions of the RF signal through the area 305 for each of theplurality of times from each of the multiple T1-T3. Logically, such athree transmitter-three receiver arrangement provides nine T-R pairingsfor the analysis (each of the three transmitters T1-T3 each pairedlogically with each of the three receivers R1-R3). Accordingly, each ofthe R1-R3 is configured to detect a metric of the received RF, which thesystem (e.g. at one or more nodes or another processor in communicationwith the receivers) uses to count occupant(s) in its own region (room360 or the hallway 380) based on receipt of the multiple RF signalsreceived globally from the multiple T1-T3 in the area 305.

Thus, for example, a RF perturbation caused by a person in room 380 isdetected by the T1/R1 and T2/R2 (in the room 360), each of whichgenerates a signal indicator data for the heuristic analysis. A personin the room 360 can also trigger a response in the hallway 390 detectedby the T3/R3 in the hallway 380, but at a lower signal level. A signallevel threshold may be used to reject the false positive in the hallway390. A similar threshold approach may be implemented to prevent falsepositives at the nodes i.e. T3/R3 in the hallway 390.

In one example, it is desired to determine an occupancy count in theroom 380. The heuristic algorithm is configured to processing indicatordata from R1 and R2 to detect one of an inaccurate occupancy count inthe room 380 since each of R1 and R2 receives RF signals not only fromthe T1 and the T2 in the room 380 but also receives RF signals from theT3 in the hallway 180. Accordingly, the heuristic algorithm is appliedto allow processing of indicator data from the R1 and R2 in the room 160to ignore/eliminate the RF signals received from the R3, which aregenerated by the T3 due to the presence of the number of occupants inthe hallway 380 and/or multipath returns of signals generated by the T1and T2 in the room 180 but received due to or modified by the presenceof number of occupants in the hallway 380. In one implementation, theoccupancy count in the room 360 is detected according to a ML occupancycounting procedure as will be described below with respect to FIGS. 4A,4B and 4C.

Referring to FIG. 4A, there is shown a functional block diagram of anexample of a heuristic occupancy counting system 400 configured tofunction on a radio frequency (RF) wireless communication network. Asillustrated, the heuristic occupancy counting system 400 includes aplurality of lighting elements 402 a-402 n in the physical space/area305 such as a room and a hallway etc. as described above with respect toFIG. 3. In one implementation, an indoor environment is described, butit should be readily apparent that the systems and methods describedherein are operable in external environments as well.

In one implementation, the system 402 includes the six intelligentsystem nodes 332, 334, 336, 338, 340, and 342 as described with respectto FIG. 3 above. As discussed above, each such system node has anintelligence capability to transmit and receive data and process thedata. Each system node, for example, may include a receiver (R) and/or atransmitter (T) along with another component used in lightingoperations. In one example, a system node includes a light source and isconfigured as a lighting device. In another example, a system nodeincludes a user interface component and is configured as a lightingcontroller. In another example the system node includes a switchablepower connector and is configured as a plug load controller. In afurther example, a system node includes a detector as well as relatedcircuitry and is configured as a lighting related sensor. The systemnodes 332, 334, and 336 include T1, T2 and T3 respectively; and thesystem nodes 338, 340 and 342 include R1, R2 and R3 respectively.

In the implementation shown in FIG. 4A, each of the system nodes 332,334, 336, 338, 340 and 342 is integrated in one of the lighting elements402 a-402 n such that the system node 332 is integrated in the lightingelement 402 a, the system node 334 is integrated in the lighting element402 b, the system node 336 is integrated in the lighting element 402 cthe system node 338 is integrated in the lighting element 402 d thesystem node 340 is integrated in the lighting element 402 e the systemnode 342 is integrated in the lighting element 402 n. Even though, thesystem nodes 332, 334, 336, 338, 340 and 342 are shown to be integratedin the lighting elements 202 a, 402 b and 402 c, 402 d, 402 e and 402 nrespectively, it should be apparent that the system nodes 332, 334, 336,338, 340 and 342 are integrated in different lighting devices among thelighting devices 402 a-402 n. In another alternate implementation, twoor more of the system nodes 332, 334, 336, 338, 340 and 342 may beintegrated in the same lighting element. For example, the system node332 and the system node 338 may both be incorporated in the lightingelement 402 a or in another example, the system node 334 and the systemnode 340 may both be incorporated in the lighting element 402 b.

As described above, the Tx is configured to transmit RF signals and eachof the Rx is configured to receive signals from each Tx. In oneimplementation, each of the lighting elements 402 a-402 n includes alight source 406, and a system node containing the source 206 or coupledto and operation together with the source 406 and is configured as aluminaire, for example, may take the form of a lamp, light fixture, orother luminaire that incorporates the light source, where the lightsource by itself contains no intelligence or communication capability,such as one or more LEDs or the like, or a lamp (e.g. “regular lightbulbs”) of any suitable type. The light source 406 is configured toilluminate some or all of the area 405. In one example, each of somenumber of individual the light sources 406 is configured to illuminate aportions or a regions of the area 405. Typically, a lighting system willinclude one or more other system nodes, such as a wall switch, a plugload controller, or a sensor.

In one implementation, the system 400 includes processing circuitry 416coupled to the receivers R1 and R2. In one implementation, theprocessing circuitry is coupled to one or more of the lighting elements402 a-402 n. In an alternate implementation, the processing circuitry416 is coupled to the system 400 via a network (not shown). In oneimplementation, the processing circuitry 416 is implemented in aprocessor executing software or firmware of configured to determineoccupancy count in the area 305 or the region (for example, room 360) inthe area 305, although other circuitry or processor-basedimplementations may be used. In one implementation, the processingcircuitry 216 is implemented in firmware of a processor in or more ofthe R1 node, R2 node or R3 node.

In one implementation, the system 400 includes a controller 418 coupledto the processing circuitry 416. In one implementation the controller418 may be the same or an additional processor configured to controloperations of elements in the system 400 in response to determination ofoccupancy count in the area 305 or a region (for example, room 360) inthe area 305. For example, in an alternate implementation, thecontroller 418 is configured to process a signal to control operation ofone or more light sources 406 a-406 n. In one alternate implementation,the controller 418 is configured to turn ON one or more light sources406 a-406 n upon the occupancy count of one or more determined by theprocessing circuitry 416. In one implementation, the controller 418 isconfigured to turn OFF one or more light sources 406 a-406 n upon theoccupancy count of zero determined by the processing circuitry 416. Inanother implementation, upon the occupancy count in the area 305, thecontroller 418 may be configured to provide other control and managementfunctions in the area such as heating, ventilation and air conditioning(HVAC), heat mapping, smoke control, equipment control, securitycontrol, etc. instead of or in addition to control of the lightsource(s). Accordingly, the system 400 is configured to function on theRF wireless communications network in accordance with an implementationof a local control of light source(s) in the lighting element(s), aswell as other automation control of energy, equipment, operational andmanagement, as discussed above, of the area.

In yet another implementation, the controller 418 communicates theoccupancy count to the lighting network via a data packet. The datapacket is received by one or more luminaires in the lighting network,which are configured to turn ON or OFF the light source(s) 406 and/or inthe luminaire or another network node to provide automation of otherenergy control, equipment control, operational control and managementsystems (e.g. HVAC, heat mapping, smoke control, equipment control,security control) in the area 105 based on the occupancy count providedin the data packet. Accordingly, the system 400 communicates theoccupancy count with other networks. In another alternateimplementation, the controller 418 is coupled to the system 400 via anetwork (not shown). Accordingly, the system 400 is configured tofunction on the RF wireless communication network in accordance with animplementation of a global control of light source(s) in lightingelement(s), as well as other automation control of energy, equipment,operational and management, as discussed above, of the area in alighting system.

In one implementation, the system nodes typically include a processor,memory and programming (executable instructions in the form of softwareand/or firmware). Although the processor may be a separate circuitry(e.g. a microprocessor), in many cases, it is feasible to utilize thecentral processing unit (CPU) and associated memory of a micro-controlunit (MCU) integrated together with a transceiver in the form of asystem on a chip (SOC). Such an SOC can implement the wirelesscommunication functions as well as the intelligence (e.g. including anyprocessing or controller capabilities) of the system node.

In examples discussed in more detail later, system nodes often mayinclude both a transmitter and a receiver (sometimes referenced togetheras a transceiver), for various purposes. At times, such atransceiver-equipped node may use its transmitter as part of a heuristicoccupancy sensing operation; and at other times such atransceiver-equipped node may use its receiver as part of a heuristicoccupancy counting operation. Such transceiver equipped nodes alsotypically include a processor, memory and programming (executableinstructions in the form of software and/or firmware). Although theprocessor may be a separate circuitry (e.g. a microprocessor), in manycases, it is feasible to utilize the central processing unit (CPU) andassociated memory of a micro-control unit (MCU) integrated together withphysical circuitry of a transceiver in the form of a system on a chip(SOC). Such an SOC can implement the wireless communication functions aswell as the intelligence (e.g. including any processing or controllercapabilities) of the system node.

Although the system nodes 332-342 of FIG. 4A illustrate animplementation of a single Tx and a single Rx in each of the nodes, thesystem 400 may include other implementations such as multiple Txs in oneor more nodes. Also, FIG. 4A illustrates the implementation of a singleRx in each of the nodes, the system 400 may include otherimplementations such as multiple Rx in one or more nodes. In theillustrated implementation, the system 400 includes multiple lightingelements 406 a-406 n with either the Tx or the Rx, however, the multiplelighting elements 406 a-406 n of the system 400 may include one or moreTx and one or more Rx (see FIG. 7).

For discussion of an initial example of a heuristic RF-based occupancycounting operation, assume that the system 400 includes just theelements shown in FIG. 4A. In one example, each of the system nodes332-342 includes the capabilities to communicate over two different RFbands, although the concepts discussed herein are applicable to devicesthat communicate with luminaires and other system elements via a singleRF band. Hence, in the dual band example, the Tx/Rx may be configuredfor sending and receiving various types of data signals over one band,e.g. for the RF transmission and reception leading to occupancycounting. The other band may be used or for pairing and commissioningmessages over another band and/or for communications related todetection of RF or higher level occupancy counting functions, e.g.between receivers R1 and R2 and the controller 420 or the processingcircuitry 416. For example, the Tx and Rx are configured as a 900 MHztransmitter and receiver for communication of a variety of system oruser data, including lighting control data, for example, commands toturn light son/OFF, dim up/down, set scene (e.g., a predetermined lightsetting), and sensor trip events. Alternatively, the Tx and Rx may beconfigured as a 2.4 GHz transmitter and receiver for Bluetooth lowenergy (BLE) communication of various messages related to commissioningand maintenance of a wireless lighting system and/or to communicateresults of processing functions in relation to occupant counting.

In one implementation, benefits of the system include the ability totake advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installedin a location in the area 305, and because the system passively monitorssignal broadcasts in the area 305 at a plurality of times, the heuristicoccupancy counting functionality does not require (does not rely on) theoccupants to carry any device.

At a high level, the T1 transmits a RF spectrum (RF) signal at aplurality of times. The transmission may be specifically for theoccupancy counting. In some cases, however, where the transmitter is inanother lighting device or other lighting system element (e.g. a sensoror a wall switch), the transmissions maybe regular lighting relatedcommunications, such as reporting status, sending commands, reportingsensed events, etc. Each of the R1-R3 receives the transmissions of theRF signal from each of the T1-T3 through the area 305 during each of theplurality of times. Each of the R1-R3 generates an indicator data of oneor more characteristics of the received RF signal at each of theplurality of times. Some of examples of the characteristics include butare not limited to received signal strength indicator (RSSI) data, biterror rate, packet error rate, phase change etc. or a combination of twoor more thereof. The RSSI data represents measurements of signalstrength of the received RF. The bit error rate is rate of incorrectbits in received RF signals versus total number of bits in thetransmitted RF signals. The packet error rate is rate of incorrectpackets in received RF signals versus total number of packets thetransmitted RF signals. Phase change is a change of phase of a receivedRF signal compared to previous reception of the RF signal (typicallymeasured between the antennas spaced apart from each other). For thepurpose of the present description, we use RSSI data as thecharacteristics of the RF signal for processing by each of the R1-R3receives to generate as the indicator data. Each of the R1-R3 measuresthe signal strength of the RF signal received from transmitter T1 andgenerates the RSSI data based on the signal strength. The signalstrength of each of the RF signal varies over time based whether anoccupant exists or there are a number of occupants in a path betweeneach of the T1-T3 and each of the R1-R3 in the area 305.

For each time, each of the receivers R1-R3 supplies the generatedindicator data of one or more characteristics of the received RF signalto the processing circuitry 316. In one implementation using RSSI as thecharacteristic of interest, the processing circuitry 316 obtains thegenerated RSSI data at each of the plurality of times from the variousreceivers R1-R3 and utilizes a heuristic algorithm to determineoccupancy count in the area 305 or the region (for example, room 360) inthe area 305 as described in greater detail herein below.

In one implementation, that takes advantage of the machine learning (ML)capability of the heurist algorithm, the system 400 includes a trusteddetector 430, which provides a known occupancy count value (similar tothe “known answer” as discussed above). Input from the trusted detector430 to “learn” so as to improve performance. The trusted detector 430 inthe example may be a standard occupancy sensor, such as passive infraredoccupancy detector or a camera based occupancy counting system.Specifically, the trusted detector 430 provides a known occupancy countvalue for an accurate occupancy count in the area 305 or the region (forexample, room 360) in the area 305 at each of the multiple times. In oneimplementation, the known occupancy count value is pre-determined priorto heuristically determining the occupancy count in the area 305 or theregion (for example, room 360) in the area 305.

In one implementation, the processing circuitry 416 obtains theindicator data of the RF signal generated for multiple times (ta-tn)from each of the R1-R3. The processing circuitry 416 applies one of aheuristic algorithm heuristic algorithm coefficient (heuristic algorithmcoefficient) among a set of heuristic algorithm heuristic algorithmcoefficients to each of the indicator data from each of the R1-R3 togenerate an indicator data metric value for each of the indicator datafrom each of the R1-R3 for the times to-tn. Each heuristic algorithmcoefficient among the set of heuristic algorithm coefficients may berandomly selected at an initial stage of training. In oneimplementation, a set of heuristic algorithm coefficients are utilizedto determine count in the entire area 305. In one implementation, adifferent set of heuristic algorithm coefficients are utilized todetermine the occupancy count in the sub-area (example, room 360) of thearea 305. As such, the different set of heuristic algorithm coefficientsare selected to reject false positives such as transmission signals fromT3 and received signals from R3 that are not part of the room 360. Inone implementation, the heuristic algorithm is trained using theappropriate set of heuristic algorithm coefficients to determine theoccupancy count in the entire area 305 or the sub-area of the area, forexample, the room 360. As discussed above, training can take place aheadof the time (before product shipment/commissioning) or in the field asan on-going optimization to reduce false positives in determining anoccupant count. Also as discussed above, the training is executed by thetrusted detector. Accordingly, the occupancy count as discussed belowwith respect to the area may include the entire area 305 or a sub-area(example room 360) of the entire area 305.

In one implementation, a heuristic algorithm coefficient is a variable.In one implementation, a value of a heuristic algorithm coefficientapplied to an indicator data from R1 is the same as a value of aheuristic algorithm coefficient applied to another indicator data thatis from R2. In another implementation, a value of a first heuristicalgorithm coefficient applied to an indicator data from the R1 isdifferent from value of another (second) heuristic algorithm coefficientapplied to another indicator data from R2. In one implementation, theprocessing circuitry 416 processes the indicator data metric values tocompute metric values associated with each of the R1 and R2 at each ofthe times ta-tn. The metric values provide a measurement of each of aplurality of a probable or possible number of occupants in the area foreach of the times ta-tn. Some examples of the measurements may includebut not limited to percentage, decimal, ratio, rates etc. In oneimplementation, the processing circuitry 416 combines/adds the metricvalues associated with R1 with metric values associated with R2 and themetric values associated with R3 to compute an output metric value foreach of the plurality of probable or possible number of occupants in thearea for each of the times ta-tn. In one implementation, the processingcircuitry 416 compares each of the plurality of output metric valueswith one another to determine which of the plurality of output metricvalues has the highest value. The processing circuitry 416 determinesthat the probable number of occupants in the area with the largestoutput metric value as the occupancy count in the area for each of thetimes ta-tn. In one implementation, the processing circuitry 416compares the occupancy count in the area with the known occupancy countvalue generated by the trusted detector for each of the ta-tn.Specifically, the processing circuitry 416 compares the occupancy countin the area at each of the ta-tn with the known occupancy count value,for example, an output of the trusted detector 430 to determine anaccurate occupancy count in the area as described in greater detailbelow. In one implementation, the system 400 includes a learning module420 coupled to the processing circuitry 416 to determine whether the setof heuristic algorithm coefficients are optimized heuristic algorithmcoefficients based on the comparison by the processing circuitry 416 atthe times ta-tn to detect an accurate occupancy count in the area. Inone implementation, upon determination, that the set of heuristicalgorithm coefficients are optimized heuristic algorithm coefficients,the learning module 420 instructs the processing circuitry 416 toutilize the optimized heuristic algorithm coefficients in real time. Inone implementation, upon determination, that the set of heuristicalgorithm coefficients are not optimized heuristic algorithmcoefficients, the learning module 420 instructs the processing circuitry416 to update one or more heuristic algorithm coefficients among the setof heuristic algorithm coefficients and utilize the updated one or moreheuristic algorithm coefficients in a next time. The aboveimplementations are described in greater detail below.

In one implementation, the processing circuitry 416 determines that theoccupancy count at a time t1 among the times ta-tn is same as the knownoccupancy count value. In one implementation, the learning module 420determines, that the set of heuristic algorithm coefficients aredetermined to be optimized heuristic algorithm coefficients to beapplied to the indicator data for the time t1 to determine the accurateoccupancy count. In one implementation, the learning module 420instructs the processing circuitry 416 to utilize the optimizedheuristic algorithm coefficients to apply to each indicator data amongthe plurality of indicator data from each of the plurality of receiversfor the time t1 to detect the occupancy count in real time. Accordingly,the processing circuitry 416 applies the optimized heuristic algorithmcoefficients to determine the occupancy count in real time.

In another implementation, the processing circuitry 416 determines thatthe occupancy count at a time t1 among the times ta-tn is different thanthe known occupancy count value. The learning module 420 determines thatthe set of heuristic algorithm coefficients are not optimized heuristicalgorithm coefficients and thus updates the one or more heuristicalgorithm coefficients among the set of the heuristic algorithmcoefficients to generate updated set of heuristic algorithmcoefficients. The learning module 420 instructs the processing circuitry416 to utilize the updated set of heuristic algorithm coefficients in anext time. The processing circuitry 416 applies the updated heuristicalgorithm coefficients to corresponding indicator data from each of theR1-R3 to generate an updated indicator data metric value for each of theindicator data from each of the R1-R3 at the time t1. In oneimplementation, the processing circuitry 416 processes each of theupdated indicator data metric values to compute updated occupancy countat t1. In one implementation, the processing circuitry 416 determinesthat the updated occupancy count at the time t1 is the same as the knownoccupancy count value. As such, the learning module 420 determines thatthe updated set of heuristic algorithm coefficients are optimizedheuristic algorithm coefficients to be applied to the indicator data forthe time t1 to determine the accurate occupancy count in real time. Inanother implementation, the processing circuitry 416 determines that theupdated occupancy count is different than the known occupancy countvalue. The processing circuitry 416 and the learning module 420 repeatsthe above process for t1 until the occupancy count is same as the knownoccupancy count value to determine that the set of heuristic algorithmcoefficients corresponding to the indicator data from each of the R1-R3are the optimized heuristic algorithm coefficients for the t1 among theta-tn to accurately determine the occupancy count at real time.Accordingly, the processing circuitry 416 applies the optimizedheuristic algorithm coefficients to determine the occupancy count inreal time.

In one implementation, the occupancy count is determined for each of theindicator data at each of the ta-tn and compared with the knownoccupancy count value to determine the optimized heuristic algorithmcoefficients for each of the ta-tn to detect an accurate occupancy countin the area 305 or the region (for example, room 360) in the area 305 ofFIG. 3 at each of the ta-tn. In one implementation, the optimized set ofheuristic algorithm coefficients for each of the ta-tn are utilized bythe processing circuitry 416 to detect accurate occupancy count in thearea 305 or the region (for example, room 360) in the area 305 of FIG. 3at real time.

Referring to FIG. 4B, there is shown a functional block diagram of anexample of a heuristic occupancy counting system (system) 401 configuredto function on a radio frequency (RF) wireless communication network. Inone implementation, the system 401 is similar to the system 400 of FIG.4A except the processing circuitry 416 is coupled to the one or more ofthe lighting elements via a network 440. In one implementation, thenetwork 440 is a wireless communication network. In one example, thenetwork 440 is a BLE mesh. In one implementation, the network 440 is awired network. In one implementation, the processing circuitry 416 is acloud computing system which includes a plurality of processingservers/machines, which work together or independently to process theindicator data to determine the occupancy count in the area 305 or theregion (for example, room 360) in the area 305. In an alternateimplementation, the controller 418 is coupled to the processingcircuitry 416 via the network 440. In such alternate implementation, thecontroller 418 is a cloud computing system which includes a plurality ofprocessing servers/machines, which work together or independently tocontrol operations of one or more elements (e.g. light source 406 a-406n) of the lighting elements 402 a-402 n and/or provide automation ofother energy control, equipment control, operational control andmanagement systems (e.g. HVAC, heat mapping, smoke control, equipmentcontrol, security control) in the area 105 based on determination of theoccupancy count by the processing circuitry 416. Accordingly,implementation of the system 201 is configured to globally control thelight source(s) of the lighting element(s) as well as other automationcontrol of energy, equipment, operational and management, as discussedabove, of the area.

Referring to FIG. 4C, there is shown a functional block diagram of anexample of a heuristic occupancy counting system (system) 402 configuredto function on a radio frequency (RF) wireless communication network. Inone implementation, the system 402 is similar to the system 400 of FIG.4A except the processing circuitry 416 is integrated in a lightingelement 402. The processing circuitry 416 functions to process theindicator data to determine the occupancy count in the area 305 or theregion (for example, room 360) in the area 305 as discussed in detailabove. The system 402 also includes plurality of lighting elements 407a-407 n including the lighting element 402. Although, not illustrated,in an alternate implementation, the processing circuitry 416 isintegrated in one of the plurality of lighting elements 407 a-407 n.Each of the plurality of lighting elements includes a correspondinglight source among a plurality of light sources 407 a-407 n.

In one implementation, the processing circuitry 416 includes a neuralnetwork as a training method to determine occupancy count of one of thearea 105 of FIG. 1 or 305 of FIG. 3. Referring to FIG. 5, there is shownan example of a neural network 500. The neural network 500 includes aninput layer 502 of input nodes 502 a-502 i, at least one middle layer504 of middle nodes 504 a-504 j and an output layer 510 of output nodes510 a-510 n. Although, the middle layer 504 includes ten nodes, it isknown to one of ordinary skill that the middle layer 504 may include anynumber of nodes, the number likely to be larger than the number of inputnodes. Even though only one middle layer is shown, it known to one ofordinary skill in the art that more than one middle layer of nodes maybe implemented in the neural network 500. As shown, each of the inputnodes 504 a-504 i is coupled to each of the middle nodes 504 a-504 j andeach of the middle nodes 504 a-504 j is coupled to each of the outputnodes 510 a-510 n. In one implementation, each of the middle nodes 504a-504 j in the middle layer 504 includes a corresponding bias constantba-bi unique to that node. The bias constants ba-bi are initiallyrandomly assigned. In one implementation, each connection from each ofthe input nodes 502 a-502 i to each of the middle nodes 504 a-504 jincludes a corresponding weight (Wa-Wi) unique to the connection. Theweights Wa-Wi are initially randomly assigned. In one implementation,the bias constants ba-bi and the weights Wa-Wi are the plurality ofheuristic algorithm coefficients as described above.

The input layer of nodes 502 a-502 i includes the RSSI data R11, R12,R13, R21, R22, R23, R31, R32 and R33. Specifically, input node 502 aincludes R11, input node 502 b includes R12, input node 502 c includesR13, input node 502 d includes R21, input node 502 e includes R22, inputnode 502 f includes R23, input node 502 g includes R31, input node 502 hincludes R32, and input node 502 i includes R33. As such, number ofinput nodes in the input layer of nodes 502 a-502 i is equal to numberof RSSI data R11, R12, R13, R21, R22, R23, R31, R32 and R33. In oneimplementation, a forward propagation including propagation function andan activation function is executed in the neural network as describedherein below.

An output of each of the input nodes 502 a-502 i is an input to each ofthe middle nodes 504 a-504 i in the middle layer. In one implementation,the forward propagation includes a propagation function executed at eachof the middle nodes, 504 a-504 i to generate propagation functionvalues. Specifically, the propagation function is determined bymultiplying each of the RSSI data, R11, R12, R13, R21, R22, R23, R31,R32 and R33 with its corresponding weight (W) among the Wa-Wi and addedwith its corresponding bias constant (b) among the ba-bi of each of themiddle nodes 504 a-504 i resulting in a propagation value Za-Zi at eachof the middle nodes 504 a-504 i, which is summed together into a singlepropagation value Zj as shown below:

$z_{j}^{l} = {{\sum\limits_{k}{w_{jk}^{l}R_{k}^{l - 1}}} + b_{j}^{l}}$

The single propagation value Zj is fed into the activation functionexecuted in each of the plurality of the output nodes 510 a-510 nresulting in an output metric value, aj for each of the output nodes 510a-510 n as shown herein below:

a _(j) ^(l) =f(z _(j) ^(l))

As discussed above, each of the output metric values represent theprobable or possible number of occupants in the area. In oneimplementation, the output value is computed for each of the RSSI dataat multiple times (ta-tn). In one implementation, each output metricvalue computed at each time among the multiple times (ta-tn) is comparedwith the known occupancy count value As discussed above, the knownoccupancy count value is a “known answer” computed from a trusteddetector such as the passive infrared occupancy detector or a camera foreach of the times among the multiple times (ta-tn).

In one implementation, the output metric values includes 40 percent forpossibly 2 occupants in the area, 30 percent for possibly 4 occupants inthe area, 20 percent for possible 7 occupants in the area and 10 percentfor possible 10 occupants in the area for a time t1 among the timesta-tn. Since the 40 percent is the highest percent, the occupancy countwould be determined to be 2 in the area for the time t1. In one example,the known occupancy count value is 2 at the time t1. The processingcircuitry 216 compares the known occupancy count value of 2 at the timet11 with the occupancy count of 2 and since it is the same value theoccupancy count of 2 is determined to be an accurate occupancy count inthe area at the time t1. Accordingly, the corresponding weights Wa-Wiand the bias constants ba-bi are considered to be optimized heuristicalgorithm coefficients and these optimized heuristic algorithmcoefficients are utilized in the forward propagation as described aboveto detect an occupancy count in the area at real time. In anotherexample, the known occupancy value is 4 at the time t1. The processingcircuitry 216 compares the known occupancy count value of 4 at the timet1 with the occupancy count of 2 and since it is a different value, theoccupancy count of 2 is determined to be an inaccurate occupancy countin the area at the time t1. Accordingly, one or more weights Wa-Wi maybe updated using a second gradient descent function as shown below:

$w_{jk}^{\prime} = {w_{jk} - {\eta \frac{\partial C}{\partial w_{jk}}}}$

Further, one or more bias constants ba-bi may be updated using the thirdgradient descent function as shown herein below:

$b_{j}^{\prime} = {b_{j} - {\eta \frac{\partial C}{\partial b_{j}}}}$

W is the weight, b is the bias constant, n is the learning rate, C isthe cost (loss) function. C is the difference between the computedoutput value and the true occupancy or non-occupancy value. C isminimized by taking the gradient with respect to the heuristic algorithmcoefficients. In one implementation, a backward propagation function isapplied to the neural network 500 using the one or more updated valuesof the weights, Wa-Wi and/or the one or more updated bias constantsba-bi. In one implementation, the backward propagation function includesproviding the one or more updated values of the weights Wa-Wi and/or oneor more updated bias constants ba-bi at each of the output nodes 510 a410 n and then cascading backwards towards the input node 502 byapplying the one or more updated weights Wa-Wi and/or the one or moreupdated values of the bias constants ba-bi cascade backwards at each ofthe corresponding middle nodes 504 a-504 j in the middle layer 504(including any additional middle nodes in additional middle layers notshown).

In one implementation, an updated occupancy count is generated for thet1 with the one or more updated weights Wa-Wi and/or the one or moreupdated bias constants ba-bi using the forward propagation as describedabove. In one implementation, the updating of Wa-Wi using the secondgradient function and/or of the ba-bi using the third gradient functionas described above, backward propagation and the forward propagation arerepeated until the occupancy count is determined to be of the same valueas the known occupancy count value at the time t1. In oneimplementation, upon determination of the updated occupant count beingequivalent to the known occupancy count value, the corresponding updatedWa-Wi and/or the updated ba-bi are utilized in the forward propagationas described above to determine the occupancy count in the area 105and/or 305 at a real time.

FIG. 6A illustrates an example of a flowchart of a method 601 for asystem level determination of an occupancy count for multiple times inarea 105 of a system of FIGS. 2A-2C. As discussed above, the system isdisposed within a physical space/area such as a room, corridor, hallway,or doorway. In one implementation, indoor environment is described, butit is known to one of ordinary skill that the systems and methodsdescribed herein are operable in external environments as well. In oneimplementation, the method 601 is implemented by the processingcircuitry 216 of FIGS. 2A-2C. At block 603, an indicator data generatedat each of the plurality of times from each of the plurality ofreceivers configured to receive RF spectrum (RF) signals from a RFtransmitter in an area is obtained. As discussed above, some of thecharacteristics include but are not limited to received signal strengthindicator (RSSI) data, bit error rate, packet error rate, phase changeetc. or a combination of two or more thereof. At block 605, theindicator data generated at each of the plurality of times from each ofthe plurality of receivers in the area is processed. In oneimplementation, the processing includes applying, at each respective oneof the plurality of times, a RF signal computation to analyze theindicator data of the RF signals from each of the plurality ofreceivers. In another implementation, the processing includes applying aheuristic algorithm to the indicator data of the RF signals from each ofthe plurality of receivers to compute an output metric value for each ofa plurality of probable number of occupants in the area. At block 607,an occupancy count in the area at each of the plurality of times isdetermined based on the processed indicator data. In one implementation,the determining includes comparing the analyzed indicator data with apre-determined number of the occupants in the area and determine one ofthe pre-determined number of occupants to be the occupancy count in thearea that best matches with the analyzed indicator data. In anotherimplementation, the determining includes comparing each one of theplurality of occupant values with another one of the plurality of outputmetric values to determine one of the plurality of computed outputmetric values as having a largest value among the plurality of computedoutput metric values; and determining the probable number of occupantsassociated with the computed output metric value having the largestvalue as the occupancy count in the area.

FIG. 6B illustrates an example of a flowchart of a method 600 forheuristically determining an occupancy count for multiple times in area105 of a system of FIGS. 2A-2C or the area 305 of the system of FIGS.4A-4C. As discussed above, the system is disposed within a physicalspace/area such as a room, corridor, hallway, or doorway. In oneimplementation, indoor environment is described, but it is known to oneof ordinary skill that the systems and methods described herein areoperable in external environments as well. In one implementation, themethod 600 is implemented by the processing circuitry 216 and thelearning module 220 of FIG. 2. In one implementation, the method 600 isimplemented by the processing circuitry 416 and the learning module 420of FIG. 4.

At block 602, an indicator data generated at each of the plurality oftimes from each of the plurality of receivers configured to receive RFspectrum (RF) signals from one or more RF transmitters in an area isobtained. As discussed above, some of the characteristics include butare not limited to received signal strength indicator (RSSI) data, biterror rate, packet error rate, phase change etc. or a combination of twoor more thereof. At block 604, at each respective one of the pluralityof times, a heuristic algorithm coefficient among a set of heuristicalgorithm coefficients is applied to each of the indicator data fromeach of the plurality of receivers for the respective time. In oneimplementation, during the initial stage of the training, each of theheuristic algorithm coefficients among the set of heuristic algorithmcoefficients are randomly selected. At block 606, at each respective oneof the plurality of times, generate an indicator data metric value foreach of the indicator data from each of the plurality of receivers forthe respective time based on results of the applications of theheuristic algorithm coefficients to the indicator data. At block 608, ateach respective one of the plurality of times, each of the indicatordata metric value for each of the indicator data is processed to computea plurality of metric values (representing a plurality of a probablenumber of occupants) for the respective receiver for the respectivetime. In one implementation, each of the plurality of metric valuesprovide a measurement of each of a plurality of a probable number ofoccupants in the area at the respective time. At block 610, each of theplurality of metric values is combined to compute an output value foreach of the plurality of probable number of occupants in the area at therespective time. At block 612, each of the plurality of output valuesare compared with one another to determine an output value among theplurality of output values having a largest value. At block 614, theprobable number of occupants associated with the output value having thelargest value is determined to be an occupancy count in the area.

At block 616, at each respective one of the plurality of times, thedetermined occupancy count is compared with a known occupancy countvalue. At block 618, at each of the respective one of the plurality oftimes, a decision is made whether the determined occupancy count isequivalent to the known occupancy count value. When at block 618, it isdetermined that determined occupancy count is equivalent to the knownoccupancy count value, then at block 620, it is determined that the setof heuristic algorithm coefficients are optimized heuristic algorithmcoefficients. At block 622, the optimized heuristic algorithmcoefficients are utilized to apply to each indicator data for theoccupancy count in the area at a real time. When at block 618, it isdetermined that the set of heuristic algorithm coefficients are notoptimized heuristic algorithm coefficients, then at block 624, at eachof the respective one of the plurality of times, one or more of the setof heuristic algorithm coefficients are updated to generate an updatedset of heuristic algorithm coefficients. In one implementation, themethod is repeated from block 604 for the updated set of heuristicalgorithm coefficients until it is determined that the updated set ofheuristic algorithm coefficients are optimized heuristic algorithmcoefficients to detect an accurate occupancy count at each respectiveone of the plurality of times.

FIG. 7 is a functional block diagram illustrating an example relating toa system of wireless networked devices that provide a variety oflighting capabilities and may implement RF-based occupancy counting. Thewireless networked devices also provide communications in support oflighting functions such as turning lights On/OFF, dimming, set scene, orsensor trip events and may implement RF-based occupancy counting. Itshould be understood that the term “lighting control device” means adevice that includes a controller (Control/XCR module or micro-controlunit) that executes a lighting application for communication over awireless lighting network communication band, of control and systemsoperations information during control network operation over thelighting network communication band.

A lighting system 702 may be designed for indoor commercial spaces,although the system may be used in outdoor or residential settings. Asshown, system 702 includes a variety of lighting control devices, suchas a set of lighting devices (a.k.a. luminaires) 104 a-104 n (lightingfixtures), a set of wall switch type user interface components (a.k.a.wall switches) 720 a-720 n, a plug load controller type element (a.k.a.plug load controller) 730 and a sensor type element (a.k.a. sensor) 735.Daylight, ambient light, or audio sensors may embedded in lightingdevices, in this case luminaires 704 a-704 n. RF wireless occupancycounting as described above is implemented in one or more of theluminaires 704 a-704 n to enable occupancy/non-occupancy based controlof the light sources. One or more luminaires may exist in a wirelessnetwork 750, for example, a sub-GHz or Bluetooth (e.g. 2.4 GHz) networkdefined by an RF channel and a luminaire identifier.

The wireless network 750 may use any available standard technology, suchas WiFi, Bluetooth, ZigBee, etc. An example of a lighting system using awireless network, such as Bluetooth low energy (BLE), is disclosed inpatent application publication US 20160248506 A1 entitled “System andMethod for Communication with a Mobile Device Via a Positioning SystemIncluding RF Communication Devices and Modulated Beacon Light Sources,”the entire contents of which are incorporated herein by reference.Alternatively, the wireless network may use a proprietary protocoland/or operate in an available unregulated frequency band, such as theprotocol implemented in nLight® Air products, which transport lightingcontrol messages on the 900 MHz band (an example of which is disclosedin U.S. patent application Ser. No. 15/214,962, filed Jul. 20, 2016,entitled “Protocol for Lighting Control Via a Wireless Network,” theentire contents of which are incorporated herein by reference). Thesystem may support a number of different lighting control protocols, forexample, for installations in which consumer selected luminaires ofdifferent types are configured for a number different lighting controlprotocols.

The system 702 also includes a gateway 752, which engages incommunication between the lighting system 702 and a server 705 through anetwork such as wide area network (WAN) 755. Although FIG. 7 depictsserver 705 as located off premises and accessible via the WAN 755, anyone of the luminaires 704 a-704 n, for example are configured tocommunicate occupancy count in an area to devices such as the server 705or even a laptop 706 located off premises.

The lighting control 702 can be deployed in standalone or integratedenvironments. System 702 can be an integrated deployment, or adeployment of standalone groups with no gateway 752. One or more groupsof lighting system 702 may operate independently of one another with nobackhaul connections to other networks.

Lighting system 702 can leverage existing sensor and fixture controlcapabilities of Acuity Brands Lighting's commercially available nLight®wired product through firmware reuse. In general, Acuity BrandsLighting's nLight® wired product provides the lighting controlapplications. However, the illustrated lighting system 704 includes acommunications backbone and includes model-transport, network, mediaaccess control (MAC)/physical layer (PHY) functions.

Lighting control 702 may comprise a mix and match of various indoorsystems, wired lighting systems (nLight® wired), emergency, and outdoor(dark to light) products that are networked together to form acollaborative and unified lighting solution. Additional control devicesand lighting fixtures, gateway(s) 750 for backhaul connection, time synccontrol, data collection and management capabilities, and interoperationwith the Acuity Brands Lighting's commercially available SensorViewproduct may also be provided.

FIG. 8 is a block diagram of a lighting device (in this example, aluminaire) 804 that operates in and communicates via the lighting system702 of FIG. 7. Luminaire 804 is an integrated light fixture thatgenerally includes a power supply 805 driven by a power source 800.Power supply 805 receives power from the power source 800, such as an ACmains, battery, solar panel, or any other AC or DC source. Power supply805 may include a magnetic transformer, electronic transformer,switching converter, rectifier, or any other similar type of circuit toconvert an input power signal into a power signal suitable for luminaire804.

Luminaire 804 furthers include an intelligent LED driver circuit 810,control/XCVR module 815, and a light emitting diode (LED) light source820. Intelligent LED driver circuit 810 is coupled to LED light source820 and drives that LED light source 820 by regulating the power to LEDlight source 820 by providing a constant quantity or power to LED lightsource 320 as its electrical properties change with temperature, forexample. The intelligent LED driver circuit 810 includes a drivercircuit that provides power to LED light source 820 and a pilot LED 817.The pilot LED 817 may be included as part of the control/XCVR module315. Intelligent LED driver circuit 810 may be a constant-voltagedriver, constant-current driver, or AC LED driver type circuit thatprovides dimming through a pulse width modulation circuit and may havemany channels for separate control of different LEDs or LED arrays. Anexample of a commercially available intelligent LED driver circuit 810is manufactured by EldoLED.

LED driver circuit 810 can further include an AC or DC current source orvoltage source, a regulator, an amplifier (such as a linear amplifier orswitching amplifier), a buck, boost, or buck/boost converter, or anyother similar type of circuit or component. LED driver circuit 810outputs a variable voltage or current to the LED light source 820 thatmay include a DC offset, such that its average value is nonzero, and/oran AC voltage.

Control/XCR module 815 includes power distribution circuitry 825 and amicro-control unit (MCU) 830. As shown, MCU 830 is coupled to LED drivercircuit 810 and controls the light source operation of the LED lightsource 820. MCU 830 includes a memory 322 (volatile and non-volatile)and a central processing unit (CPU) 823. The memory 822 includes alighting application 827 (which can be firmware) for both occupancycounting and lighting control operations. The power distributioncircuitry 825 distributes power and ground voltages to the MCU 830,wireless transmitter 808 and wireless receiver 810, to provide reliableoperation of the various circuitry on the sensor/processing circuitry815 chip.

Luminaire 804 also includes a wireless radio communication interfacesystem configured for two way wireless communication on at least oneband. Optionally, the wireless radio communication interface system maybe a dual-band system. It should be understood that “dual-band” meanscommunications over two separate RF bands. The communication over thetwo separate RF bands can occur simultaneously (concurrently); however,it should be understood that the communication over the two separate RFbands may not actually occur simultaneously.

In our example, luminaire 804 has a radio set that includes radiotransmitter 808 as well as a radio receiver 810, together forming aradio transceiver. The wireless transmitter 808 transmits RF signals onthe lighting network. This wireless transmitter 808 wirelesscommunication of control and systems operations information, duringluminaire operation and during transmission over the first wirelesscommunication band. The wireless receiver carries out receiving of theRF signals from other system elements on the network and generating RSSIdata based on signal strengths of the received RF signals. If provided(optional) another transceiver (Tx and Rx) may be provided, for example,for point-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the luminaire 804 may have a radio set forming a secondtransceiver (shown in dotted lines, transmitter and receiver notseparately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The RF spectrum or “radio spectrum” is a non-visible part of theelectromagnetic spectrum, for example, from around 3 MHz up toapproximately 3 THz, which may be used for a variety of communicationapplications, radar applications, or the like. In the discussions above,the RF transmitted and received for network communication, e.g. Wifi,BLE, Zigbee etc., was also used for occupancy counting functions, in thefrequencies bands/bandwidths specified for those standard wireless RFspectrum data communication technologies. In another implementation, thetransceiver is an ultra-wide band (also known as UWB, ultra-wide bandand ultraband) transceiver. UWB is a radio technology that can use avery low energy level for short-range, high-bandwidth communicationsover a large portion of the radio spectrum. UWB does not interfere withconventional narrowband and carrier wave transmission in the samefrequency band. Ultra-wideband is a technology for transmittinginformation spread over a large bandwidth (>500 MHz) and under certaincircumstances be able to share spectrum with other users.

Ultra-wideband characteristics are well-suited to short-distanceapplications, such as short-range indoor applications. High-data-rateUWB may enable wireless monitors, the efficient transfer of data fromdigital camcorders, wireless printing of digital pictures from a camerawithout the need for a personal computer and file transfers betweencell-phone handsets and handheld devices such as portable media players.UWB may be used in a radar configuration (emitter and deflectiondetection at one node) for real-time location systems and occupancysensing/counting systems; its precision capabilities and low power makeit well-suited for radio-frequency-sensitive environments. Anotherfeature of UWB is its short broadcast time. Ultra-wideband is also usedin “see-through-the-wall” precision radar-imaging technology, precisiondetecting and counting occupants (between two radios), precisionlocating and tracking (using distance measurements between radios), andprecision time-of-arrival-based localization approaches. It isefficient, with a spatial capacity of approximately 1013 bit/s/m². Inone example, the UWB is used as the active sensor component in anautomatic target recognition application, designed to detect humans orobjects in any environment.

The MCU 830 may be a system on a chip. Alternatively, a system on a chipmay include the transmitter 808 and receiver 810 as well as thecircuitry of the MCU 830.

As shown, the MCU 830 includes programming in the memory 822. A portionof the programming configures the CPU (processor) 823 to determineoccupancy counting in an area in the lighting network, including thecommunications over one or more wireless communication. The programmingin the memory 822 includes a real-time operating system (RTOS) andfurther includes a lighting application 827 which is firmware/softwarethat engages in communications with controlling of the light sourcebased on occupancy counting determined by the CPU 823. The lightingapplication 827 programming in the memory 822 carries out lightingcontrol operations over the lighting network 750 of FIG. 7. Theprogramming for the determination of an occupancy count in the area maybe implemented as part of the RTOS, as part of the lighting application827, as a standalone application program, or as other instructions inthe memory.

FIG. 9 is a block diagram of a wall type user interface element 915 thatoperates in and communicates via the lighting system 702 of FIG. 7. Walltype user interface (UI) element (UI element) is an integrated wallswitch that generally includes a power supply 905 driven by a powersource 900. Power supply 905 receives power from the power source 900,such as an AC mains, battery, solar panel, or any other AC or DC source.Power supply 905 may include a magnetic transformer, electronictransformer, switching converter, rectifier, or any other similar typeof circuit to convert an input power signal into a power signal suitablefor the UI element 915.

UI element 915 furthers includes an intelligent LED driver circuit 910,coupled to LED (s) 920 and drives that LED light source (LED) 920 byregulating the power to LED 820 by providing a constant quantity orpower to LED 920 as its electrical properties change with temperature,for example. The intelligent LED driver circuit 910 includes a drivercircuit that provides power to LED 920 and a pilot LED 917. IntelligentLED driver circuit 910 may be a constant-voltage driver,constant-current driver, or AC LED driver type circuit that providesdimming through a pulse width modulation circuit and may have manychannels for separate control of different LEDs or LED arrays. Anexample of a commercially available intelligent LED driver circuit 910is manufactured by EldoLED.

LED driver circuit 910 can further include an AC or DC current source orvoltage source, a regulator, an amplifier (such as a linear amplifier orswitching amplifier), a buck, boost, or buck/boost converter, or anyother similar type of circuit or component. LED driver circuit 910outputs a variable voltage or current to the LED light source 920 thatmay include a DC offset, such that its average value is nonzero, and/oran AC voltage.

The UI element 915 includes power distribution circuitry 925 and amicro-control unit (MCU) 930. As shown, MCU 930 is coupled to LED drivercircuit 910 and controls the light source operation of the LED 920. MCU930 includes a memory 922 (volatile and non-volatile) and a centralprocessing unit (CPU) 923. The memory 922 includes a lightingapplication 927 (which can be firmware) for both occupancy counting andlighting control operations. The power distribution circuitry 925distributes power and ground voltages to the MCU 930, wirelesstransmitter 908 and wireless receiver 910, to provide reliable operationof the various circuitry on the UI element 915 chip.

The UI element 915 also includes a wireless radio communicationinterface system configured for two way wireless communication on atleast one band. Optionally, the wireless radio communication interfacesystem may be a dual-band system. It should be understood that“dual-band” means communications over two separate RF bands. Thecommunication over the two separate RF bands can occur simultaneously(concurrently); however, it should be understood that the communicationover the two separate RF bands may not actually occur simultaneously.

In our example, the UI element 915 has a radio set that includes radiotransmitter 908 as well as a radio receiver 910 together forming a radiotransceiver. The wireless transmitter 908 transmits RF signals on thelighting network. This wireless transmitter 908 wireless communicationof control and systems operations information, during luminaireoperation and during transmission over the first wireless communicationband. The wireless receiver carries out receiving of the RF signals fromother system elements on the network and generating RSSI data based onsignal strengths of the received RF signals. If provided (optional)another transceiver (Tx and Rx) may be provided, for example, forpoint-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the UI element 915 may have a radio set forming a secondtransceiver (shown in dotted lines, transmitter and receiver notseparately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The MCU 930 may be a system on a chip. Alternatively, a system on a chipmay include the transmitter 908 and receiver 910 as well as thecircuitry of the MCU 930.

As shown, the UI element 915 includes a drive/sense circuitry 935, suchas an application firmware, drives the occupancy, audio, and photosensor hardware. The drive/sense circuitry 935 detects state changes(such as change of occupancy, audio or daylight sensor or switch to turnlighting on/off, dim up/down or set scene) via switches 965, such as adimmer switch, set scene switch. Switches 965 can be or include sensors,such as infrared sensors for occupancy or motion detection, occupancycount, an in-fixture daylight sensor, an audio sensor, a temperaturesensor, or other environmental sensor. Switches 965 may be based onAcuity Brands Lighting's commercially available xPoint® Wireless ES7product.

Also, as shown, the MCU 930 includes programming in the memory 922. Aportion of the programming configures the CPU (processor) 923 todetermine occupancy count in an area in the lighting network, includingthe communications over one or more wireless communication bands. Theprogramming in the memory 922 includes a real-time operating system(RTOS) and further includes a lighting application 927 which isfirmware/software that engages in communications with controlling of thelight source based on one of the occupancy count determined by the CPU923. As shown, a drive/sense circuitry detects a state change event. Thelighting application 927 programming in the memory 922 carries outlighting control operations over the lighting system 702 of FIG. 7. Theprogramming for the determination of an occupancy count in the area maybe implemented as part of the RTOS, as part of the lighting application927, as a standalone application program, or as other instructions inthe memory.

FIG. 10 is a block diagram of a sensor type element, 1015 that operatesin and communicates via the lighting system 702 of FIG. 7. Sensor typeelement is an integrated sensor detector that generally includes a powersupply 1005 driven by a power source 1000. Power supply 805 receivespower from the power source 1000, such as an AC mains, battery, solarpanel, or any other AC or DC source. Power supply 1005 may include amagnetic transformer, electronic transformer, switching converter,rectifier, or any other similar type of circuit to convert an inputpower signal into a power signal suitable for the sensor type element1015.

The sensor type element 1015 includes power distribution circuitry 1025and a micro-control unit (MCU) 1030. As shown, MCU 1030 includes amemory 1022 (volatile and non-volatile) and a central processing unit(CPU) 1023. The memory 1022 includes a lighting application 1027 (whichcan be firmware) for both occupancy counting and lighting controloperations. The power distribution circuitry 1925 distributes power andground voltages to the MCU 1030, wireless transmitter 1008 and wirelessreceiver 1010, to provide reliable operation of the various circuitry onthe sensor type element 1015 chip.

The sensor type element 1015 also includes a wireless radiocommunication interface system configured for two way wirelesscommunication on at least one band. Optionally, the wireless radiocommunication interface system may be a dual-band system. It should beunderstood that “dual-band” means communications over two separate RFbands. The communication over the two separate RF bands can occursimultaneously (concurrently); however, it should be understood that thecommunication over the two separate RF bands may not actually occursimultaneously.

In our example, the sensor type element 1015 has a radio transmitter1008 as well as radio receiver 1010 together forming a radiotransceiver. The wireless transmitter 1008 transmits RF signals on thelighting network. This wireless transmitter 1008 wireless communicationof control and systems operations information, during luminaireoperation and during transmission over the first wireless communicationband. The wireless receiver carries out receiving of the RF signals fromother system elements on the network and generating RSSI data based onsignal strengths of the received RF signals. If provided (optional)another transceiver (Tx and Rx) may be provided, for example, forpoint-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the luminaire sensor type element 1015 may have a radio setforming a second transceiver (shown in dotted lines, transmitter andreceiver not separately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The MCU 1030 may be a system on a chip. Alternatively, a system on achip may include the transmitter 1008 and the receiver 1010 as well asthe circuitry of the MCU 830.

As shown, the sensor type element 1015 includes a drive/sense circuitry1035, such as an application firmware, drives the occupancy, daylight,audio, and photo sensor hardware. The drive/sense circuitry 1035 detectsstate changes (such as change of occupancy, audio or daylight) viasensor detector(s) 1065, such as occupancy count, audio, daylight,temperature or other environment related sensors. Sensors 1065 may bebased on Acuity Brands Lighting's commercially available xPoint®Wireless ES7 product.

Also as shown, the MCU 1030 includes programming in the memory 1022. Aportion of the programming configures the CPU (processor) 1023 todetermine occupancy count in an area in the lighting network, includingthe communications over one or more different wireless communicationbands. The programming in the memory 1022 includes a real-time operatingsystem (RTOS) and further includes a lighting application 1027 which isfirmware/software that engages in communications with controlling of thelight source based on occupancy count determined by the CPU 1023. Thelighting application 1027 programming in the memory 1022 carries outlighting control operations over the lighting system 702 of FIG. 7. Theprogramming for the determination of an occupancy count in the area maybe implemented as part of the RTOS, as part of the lighting application1027, as a standalone application program, or as other instructions inthe memory.

FIG. 11 is a block diagram of a plug load controller type element (plugload element) 1115 that operates in and communicates via the lightingsystem 702 of FIG. 7. In one example, plug load element 1115 is anintegrated switchable power connector that generally includes a powersupply 1105 driven by a power source 1100. Power supply 1105 receivespower from the power source 1100, such as an AC mains, battery, solarpanel, or any other AC or DC source. Power supply 1105 may include amagnetic transformer, electronic transformer, switching converter,rectifier, or any other similar type of circuit to convert an inputpower signal into a power signal suitable for the plug load element1115.

Plug load element 1115 includes an intelligent LED driver circuit 1110,coupled to LED (s) 1120 and drives that LED light source (LED) byregulating the power to LED 1120 by providing a constant quantity orpower to LED 1120 as its electrical properties change with temperature,for example. The intelligent LED driver circuit 1110 includes a drivercircuit that provides power to LED 1120 and a pilot LED 1117.Intelligent LED driver circuit 1110 may be a constant-voltage driver,constant-current driver, or AC LED driver type circuit that providesdimming through a pulse width modulation circuit and may have manychannels for separate control of different LEDs or LED arrays. Anexample of a commercially available intelligent LED driver circuit 1110is manufactured by EldoLED.

LED driver circuit 1110 can further include an AC or DC current sourceor voltage source, a regulator, an amplifier (such as a linear amplifieror switching amplifier), a buck, boost, or buck/boost converter, or anyother similar type of circuit or component. LED driver circuit 1110outputs a variable voltage or current to the LED light source 1120 thatmay include a DC offset, such that its average value is nonzero, and/oran AC voltage.

The plug load element 1115 includes power distribution circuitry 1125and a micro-control unit (MCU) 1130. As shown, MCU 1130 is coupled toLED driver circuit 1110 and controls the light source operation of theLED 1120. MCU 1130 includes a memory 1122 (volatile and non-volatile)and a central processing unit (CPU) 1123. The memory 1122 includes alighting application 1127 (which can be firmware) for both occupancycounting and lighting control operations. The power distributioncircuitry 1125 distributes power and ground voltages to the MCU 1130,wireless transmitter 1108 and wireless receiver 1110, to providereliable operation of the various circuitry on the plug load control1115 chip.

The plug load element 1115 also includes a wireless radio communicationinterface system configured for two way wireless communication on atleast one band. Optionally, the wireless radio communication interfacesystem may be a dual-band system. It should be understood that“dual-band” means communications over two separate RF bands. Thecommunication over the two separate RF bands can occur simultaneously(concurrently); however, it should be understood that the communicationover the two separate RF bands may not actually occur simultaneously.

In our example, the plug load element 1115 has a radio set that includesradio transmitter 1108 as well as a radio receiver 1110 forming a radiotransceiver. The wireless transmitter 1108 transmits RF signals on thelighting network. This wireless transmitter 1108 wireless communicationof control and systems operations information, during luminaireoperation and during transmission over the first wireless communicationband. The wireless receiver carries out receiving of the RF signals fromother system elements on the network and generating RSSI data based onsignal strengths of the received RF signals. If provided (optional)another transceiver (Tx and Rx) may be provided, for example, forpoint-to-point communication, over a second different wirelesscommunication bands, e.g. for communication of information other thanthe control and systems operations information, concurrently with atleast some communications over the first wireless communication band.Optionally, the plug load element 1115 may have a radio set forming asecond transceiver (shown in dotted lines, transmitter and receiver notseparately shown).

The included transceiver (solid lines), for example, may be a sub GHztransceiver or a Bluetooth transceiver configured to operate in astandard GHz band. A dual-band implementation might include twotransceivers for different bands, e.g. for a sub GHz band and a GHz bandfor Bluetooth or the like. Additional transceivers may be provided. Theparticular bands/transceivers are described here by way of non-limitingexample, only.

If two bands are supported, the two bands may be for differentapplications, e.g. lighting system operational communications and systemelement maintenance/commissioning. Alternatively, the two bands maysupport traffic segregation, e.g. one band may be allocated tocommunications of the entity owning/operating the system at the premiseswhereas the other band may be allocated to communications of a differententity such as the system manufacturer or a maintenance service bureau.

The MCU 1130 may be a system on a chip. Alternatively, a system on achip may include the transmitter 1108 and the receiver 1110 as well asthe circuitry of the MCU 1130.

Plug load element 1115 plugs into existing AC wall outlets, for example,and allows existing wired lighting devices, such as table lamps or floorlamps that plug into a wall outlet, to operate in the lighting system.The plug load element 1115 instantiates the table lamp or floor lamp byallowing for commissioning and maintenance operations and processeswireless lighting controls in order to the allow the lighting device tooperate in the lighting system. Plug load element 1115 further comprisesan AC power relay 1160 which relays incoming AC power from power source1100 to other devices that may plug into the receptacle of plug loadelement 1115 thus providing an AC power outlet 1170.

Also, as shown, the MCU 1130 includes programming in the memory 1122. Aportion of the programming configures the CPU (processor) 1123 todetermine occupancy count in an area in the lighting network, includingthe communications over one or more wireless communication bands. Theprogramming in the memory 1122 includes a real-time operating system(RTOS) and further includes a lighting application 1127 which isfirmware/software that engages in communications with controlling of thelight source based on the occupancy count determined by the CPU 1123. Asshown, a drive/sense circuitry detects a state change event. Thelighting application 1127 programming in the memory 1122 carries outlighting control operations over the lighting system 702 of FIG. 7. Theprogramming for the determination of the occupancy count in the area maybe implemented as part of the RTOS, as part of the lighting application1127, as a standalone application program, or as other instructions inthe memory.

FIGS. 12 and 13 provide functional block diagram illustrations ofgeneral purpose computer hardware platforms that may be configured toimplement some or all of the processor or controller functions in theexamples described above with respect to FIGS. 1-6. The computerhardware platforms may also be used to implement server 705, theterminal computer 706 or the gateway 752 of FIG. 7.

Specifically, FIG. 12 illustrates a network or host computer platform,as may typically be used to implement a server. Specifically, FIG. 13depicts a computer with user interface elements, as may be used toimplement a personal computer or other type of work station or terminaldevice, although the computer of FIG. 13 may also act as a server ifappropriately programmed. It is believed that those skilled in the artare familiar with the structure, programming and general operation ofsuch computer equipment and as a result the drawings should beself-explanatory.

Hardware of a server computer, for example (FIG. 12), includes a datacommunication interface for packet data communication. The servercomputer also includes a central processing unit (CPU), in the form ofcircuitry forming one or more processors, for executing programinstructions. The server platform hardware typically includes aninternal communication bus, program and/or data storage for variousprograms and data files to be processed and/or communicated by theserver computer, although the server computer often receives programmingand data via network communications. The hardware elements, operatingsystems and programming languages of such server computers areconventional in nature, and it is presumed that those skilled in the artare adequately familiar therewith. Of course, the server functions maybe implemented in a distributed fashion on a number of similar hardwareplatforms, to distribute the processing load.

Hardware of a computer type user terminal device, such as a PC or tabletcomputer, similarly includes a data communication interface, CPU, mainmemory and one or more mass storage devices for storing user data andthe various executable programs (see FIG. 13).

Aspects of the methods for occupancy counting in a nodal network, asoutlined above, may be embodied in programming in (such as describedabove FIGS. 12 and 13), e.g. in the form of software, firmware, ormicrocode executable by a networked computer system such as a server orgateway, and/or a programmable nodal device. Program aspects of thetechnology may be thought of as “products” or “articles of manufacture”typically in the form of executable code and/or associated data that iscarried on or embodied in a type of machine readable medium. “Storage”type media include any or all of the tangible memory of the computers,processors or the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide non-transitory storage at any time for the software programming.All or portions of the software may at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, may enable loading of the software, fromone computer or processor into another, for example, from a managementserver or host processor (e.g. from server 705, terminal computer 706 orgateway 752 of FIG. 7) into the processing circuitry 216 of the systems200, 201 and 202 of FIGS. 2A, 2B and 2C respectively or the processingcircuitry 416 of the systems 400, 401 and 402 of FIGS. 4A, 4B and 4Crespectively. Thus, another type of media that may bear the softwareelements includes optical, electrical and electromagnetic waves, such asused across physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also may be considered as media bearing the software.As used herein, unless restricted to one or more of “non-transitory,”“tangible” or “storage” media, terms such as computer or machine“readable medium” refer to any medium that participates in providinginstructions to a processor for execution.

Hence, a machine readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-transitory storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like. It may also include storage media such asdynamic memory, for example, the main memory of a machine or computerplatform. Tangible transmission media include coaxial cables; copperwire and fiber optics, including the wires that include a bus within acomputer system. Carrier-wave transmission media can take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and light-based datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer can read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to aprocessor for execution.

Program instructions may include a software or firmware implementationencoded in any desired language. Programming instructions, when embodiedin machine readable medium accessible to a processor of a computersystem or device, render computer system or device into aspecial-purpose machine that is customized to perform the operationsspecified in the program performed by the systems 200, 201 and 202 ofFIGS. 2A, 2B and 2C respectively or the processing circuitry 416 of thesystems 400, 401 and 402 of FIGS. 4A, 4B and 4C respectively.

While the foregoing has described what are considered to be the bestmode and/or other examples, it is understood that various modificationsmay be made therein and that the subject matter disclosed herein may beimplemented in various forms and examples, and that the teachings may beapplied in numerous applications, only some of which have been describedherein. It is intended by the following claims to claim any and allapplications, modifications and variations that fall within the truescope of the present teachings.

Unless otherwise stated, all measurements, values, ratings, positions,magnitudes, sizes, and other specifications that are set forth in thisspecification, including in the claims that follow, are approximate, notexact. They are intended to have a reasonable range that is consistentwith the functions to which they relate and with what is ordinary in theart to which they pertain.

The scope of protection is limited solely by the claims that now follow.That scope is intended and should be interpreted to be as broad as isconsistent with the ordinary meaning of the language that is used in theclaims when interpreted in light of this specification and theprosecution history that follows and to encompass all structural andfunctional equivalents. Notwithstanding, none of the claims are intendedto embrace subject matter that fails to satisfy the requirement ofSections 101, 102, or 103 of the Patent Act, nor should they beinterpreted in such a way. Any unintended embracement of such subjectmatter is hereby disclaimed.

Except as stated immediately above, nothing that has been stated orillustrated is intended or should be interpreted to cause a dedicationof any component, step, feature, object, benefit, advantage, orequivalent to the public, regardless of whether it is or is not recitedin the claims.

It will be understood that the terms and expressions used herein havethe ordinary meaning as is accorded to such terms and expressions withrespect to their corresponding respective areas of inquiry and studyexcept where specific meanings have otherwise been set forth herein.Relational terms such as first and second and the like may be usedsolely to distinguish one entity or action from another withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities or actions. The terms “comprises,” “comprising,”“includes,” “including,” or any other variation thereof, are intended tocover a non-exclusive inclusion, such that a process, method, article,or apparatus that includes a list of elements does not include onlythose elements but may include other elements not expressly listed orinherent to such process, method, article, or apparatus. An elementpreceded by “a” or “an” does not, without further constraints, precludethe existence of additional identical elements in the process, method,article, or apparatus that includes the element.

The term “coupled” as used herein refers to any logical, physical orelectrical connection, link or the like by which signals produced by onesystem element are imparted to another “coupled” element. Unlessdescribed otherwise, coupled elements or devices are not necessarilydirectly connected to one another and may be separated by intermediatecomponents, elements or communication media that may modify, manipulateor carry the signals. Each of the various couplings may be considered aseparate communications channel.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various examples for the purpose of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed examples require more featuresthan are expressly recited in each claim. Rather, as the followingclaims reflect, inventive subject matter lies in less than all featuresof a single disclosed example. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separately claimed subject matter.

1. A system comprising: a plurality of lighting elements, wherein each of the plurality of lighting elements is a luminaire and comprises a light source to illuminate an area; a plurality of wireless communication transmitters for wireless radio frequency (RF) spectrum transmissions in an area, including RF spectrum transmission at a plurality of times, wherein each of the plurality of transmitters is integrated into one of the lighting elements; a plurality of wireless communication receivers integrated into other of the lighting elements, wherein each of the plurality of wireless receivers is configured to receive signals of transmissions from each of the transmitters through the area at the plurality of times, wherein each of the plurality of the receivers is configured to generate an indicator data of a signal characteristic of RF spectrum signal received from each of the transmitters at each of the plurality of times; and a processing circuitry coupled to obtain the indicator data of RF spectrum signals generated at each of the plurality of times from each of the plurality of receivers, wherein the processing circuitry is configured to: at each respective one of the plurality of times: (i) for each respective one of the receivers: (a) apply a plurality of heuristic algorithm coefficients to each indicator data of RF spectrum signal received, from each of the transmitters, in the other of the lighting elements, generated by the respective receiver, for the respective time, (b) based on results of the application of the heuristic algorithm coefficients to the indicator data, generate an indicator data metric value for each of the indicator data generated by the respective receiver for the respective time, (c) process each of the indicator data metric for each of the indicator data to compute a plurality of metric values for the respective receiver for the respective time, wherein each of the plurality of metric values provide a measurement of each of a plurality of a probable number of occupants in the area for the respective time, and (d) combine the plurality of metric values for the respective receiver to compute an output metric value for each of the plurality of probable number of occupants in the area for the respective time; and (ii) determine an occupancy count in the area at the respective time based on the computed output metric values for each of the plurality of probable number of occupants in the area.
 2. The system of claim 1 further comprising a controller coupled to the processing circuitry to control the light source in response to determination of the occupancy count in the area at each of the plurality of times.
 3. The system of claim 1 wherein: one or more of the plurality of wireless transmitters are one of a WiFi, blue tooth low energy, Zigbee, nLightAir or ultra wide band transmitters; and one or more of the plurality of wireless receivers are one of a WiFi, blue tooth low energy, Zigbee, nLightAir or ultra wide band receivers.
 4. The system of claim 1 wherein to determine the occupancy count in the area at the respective time, the processing circuitry to: compare each one of the plurality of computed output metric values with another one of the plurality of output values to determine one of the plurality of computed output metric values as having a largest value among the plurality of computed metric values; and determine the probable number of occupants associated with the computed output metric value having the largest value as the occupancy count in the area.
 5. The system of claim 4 further comprising a trusted detector, wherein the trusted detector comprises a known occupancy count value for a pre-determined number of occupants in the area at each of the plurality of times.
 6. The system of claim 5 wherein the processing circuitry is further configured to, at each respective one of the plurality of times, compare the determined occupancy count in the area with the known occupancy count value generated by the trusted detector during the respective one of the plurality of times.
 7. The system of claim 6 further comprising a learning module coupled to the processing circuitry, wherein the learning module is configured to: determine whether the plurality of the heuristic algorithm coefficients are optimized heuristic algorithm coefficients at each of the plurality of times based on the comparison.
 8. The system of claim 7 wherein upon determination of the plurality of the heuristic algorithm coefficients as the optimized heuristic algorithm coefficients, the learning module is configured to instruct the processing circuitry to utilize the optimized heuristic algorithm coefficients to apply to each indicator data from each of the plurality of receivers for determination of the occupancy count in the area at a real time.
 9. The system of claim 7 wherein upon determination of the plurality of the heuristic algorithm coefficients as not the optimized heuristic algorithm coefficients, the learning module is configured to update one or more of the plurality of heuristic algorithm coefficients and instruct the processing circuitry to utilize the updated one or more heuristic algorithm coefficients in a next time.
 10. The system of claim 9 wherein the processing circuitry to at each respective one of the plurality of times: (i) for each respective one of the receivers: (a) apply heuristic algorithm coefficients including the one or more updated heuristic algorithm coefficients to each indicator data generated by the respective receiver for the respective time, (b) based on results of the application of the heuristic algorithm coefficients including the one or more updated heuristic algorithm coefficients to the indicator data, generate an updated indicator data metric value for each of the indicator data generated by the respective receiver for the respective time, (c) process the updated indicator data metrics compute a plurality of updated metric values for the respective receiver for the respective time, wherein each of the plurality of updated metric values provide a measurement of each of a plurality of a probable number of occupants in the area for the respective time and (d) combine the plurality of updated metric values for the respective receiver to compute an updated output metric value for each of the plurality of probable number of occupants in the area for the respective time; and (ii) determine an occupancy count in the area at the respective time based on the updated computed output metric values for each of the plurality of probable number of occupants in the area.
 11. The system of claim 1, wherein the indicator data is one of a relative signal strength indicator (RSSI) data, bit error rate data, packet error rate data, or a phase change data, or a combination of two or more thereof.
 12. The system of claim 11 wherein: the processing circuitry is a neural network module, the neural network module comprise: an input layer having a plurality of input nodes, each of the plurality of input nodes include an indicator data among the plurality of indicator data; a middle layer having a plurality of middle nodes, each of the middle nodes is coupled to each of the plurality of input nodes; and an output layer having a plurality of output nodes, each of the output nodes is coupled to each of the plurality of middle nodes.
 13. The system of 12 wherein the indicator data is a relative signal strength indicator (RSSI) data and the plurality of heuristic algorithm coefficients comprise a set of weights and a set of bias constants, wherein to apply a plurality of the heuristic algorithm coefficients to the RSSI data generated by the respective receiver for the respective time, generate an RSSI data metric value for the RSSI data generated by the respective receiver for the respective time and compute a plurality of metric values for the respective receiver for the respective time, the neural network module to apply a forward propagation function, wherein the forward propagation function comprise: at each of the plurality of middle nodes: receive from each of the input nodes among the plurality of input nodes, a corresponding RSSI data among the plurality of RSSI data; apply a set of weights and a set of bias constants to each of the RSSI data, wherein to apply, each of the plurality of middle nodes to: compute a product value of each weight among the set of weights with each of the RSSI data to generate the RSSI data metric value of each of the RSSI data, wherein the weight is a connection between an input node and a corresponding middle node; add each product value with a corresponding bias constant among the set of bias constants to generate a plurality of constant values; and sum each of the plurality constant values to generate a propagation value.
 14. The system of claim 13 wherein to compute the output metric value for each of the plurality of probable number of occupants in the area at the respective time, the neural network module to: at each of the plurality of the output nodes: apply an activation function to the propagation value.
 15. The system of claim 14 wherein the neural network module to: update one or more weights among the set of weights to generate updated set of weights; and update one or more bias constants among the set of bias constants to generate updated set of bias constants.
 16. The system of claim 15 wherein the neural network module to apply a backward propagation function, wherein the backward propagation function comprise: provide, at each of the plurality of output nodes, the updated set of weights and the updated set of bias constants; and apply, at each of the plurality of middle nodes, the updated set of weights and the updated set of bias constants.
 17. The system of claim 1 wherein the processing circuitry to determine an occupancy count in a sub-area within the area.
 18. The system of claim 17 wherein the processing circuitry to reject the indicator data of RF spectrum signals generated by a receiver among the plurality of receivers located outside of the sub-area and within the area.
 19. The system of claim 17 wherein the processing circuitry to reject the indicator data generated by a receiver among the plurality of receivers of the RF spectrum signals received from a transmitter among the plurality of transmitters located outside of the sub-area and within the area.
 20. A method comprising: obtaining, in a lighting system, an indicator data generated at each of a plurality of times from each of a plurality of receivers configured to receive radio frequency (RF) spectrum signals from each of a plurality of RF transmitters in an area, wherein the lighting system comprises a plurality of lighting elements; at each respective one of the plurality of times in the lighting system: applying a plurality of heuristic algorithm coefficients to each indicator data from each of the plurality of receivers for the respective time, based on results of the application of the heuristic algorithm coefficients to the indicator data, generating an indicator data metric value for the indicator data generated by the respective receiver for the respective time, processing each of the indicator data metric for each of the indicator data to compute a plurality of metric values for the respective receiver for the respective time, wherein each of the plurality of metric values provide a measurement of each of a plurality of a probable number of occupants in the area for the respective time, combining the plurality of metric values for the respective receiver to compute an output value for each of the plurality of probable number of occupants in the area for the respective time; determining an occupancy count in the area at the respective time based on the computed output metric values for each of the plurality of probable number of occupants in the area.
 21. The method of claim 20 further comprising controlling the light source in response to determination of the occupancy count in the area at each of the plurality of times.
 22. The method of claim 20 wherein the determining comprising: comparing each one of the plurality of computed output metric values with another one of the plurality of output values to determine one of the plurality of computed output metric values as having a largest value among the plurality of computed metric values; and determining the probable number of occupants associated with the computed output metric value having the largest value as the occupancy count in the area.
 23. The method of claim 22 further comprising comparing the determined occupancy count in the area with a known occupancy count value during the respective one of the plurality of times.
 24. The method of claim 20 further comprising determining whether the plurality of the heuristic algorithm coefficients are optimized heuristic algorithm coefficients at each of the plurality of times based on the comparison.
 25. The method of claim 24 wherein upon determination of the plurality of the heuristic algorithm coefficients as the optimized heuristic algorithm coefficients, utilizing the optimized heuristic algorithm coefficients to apply to each indicator data from each of the plurality of receivers for determination of the occupancy count in the area at a real time.
 26. The method of claim 24 wherein upon determination of the plurality of the heuristic algorithm coefficients as not the optimized heuristic algorithm coefficients, updating one or more of the plurality of heuristic algorithm coefficients.
 27. The method of claim 26 wherein at each respective one of the plurality of times after the update: applying heuristic algorithm coefficients including the one or more updated heuristic algorithm coefficients to each indicator data from each of the plurality of receivers for the respective time, based on results of the application of the updated heuristic algorithm coefficients to the indicator data, generating an updated indicator data metric value for the indicator data generated by the respective receiver for the respective time, processing each of the updated indicator data metric for each of the indicator data to compute a plurality of updated metric values for the respective receiver for the respective time, wherein each of the plurality of updated metric values provide a measurement of each of a plurality of a probable number of occupants in the area for the respective time, combining the plurality of updated metric values for the respective receiver to compute an updated output value for each of the plurality of probable number of occupants in the area for the respective time; determining an occupancy count in the area at the respective time based on the computed updated output metric values for each of the plurality of probable number of occupants in the area.
 28. A system comprising: a plurality of lighting elements, wherein each of the plurality of lighting elements is a luminaire and comprises a light source to illuminate an area: a plurality of wireless communication transmitters for wireless radio frequency (RF) spectrum transmission in an area, including RF transmission at a plurality of times, wherein each of the plurality of transmitters is integrated into one of the lighting elements; a plurality of wireless communication receivers integrated into other of the lighting elements and configured to receive RF spectrum signals of transmissions from each of the plurality transmitters through the area at the plurality of times, wherein each of the plurality of the receivers is configured to generate an indicator data of a signal characteristic of received RF spectrum signal at each of the plurality of times; and a processing circuitry coupled to obtain the indicator data of the RF spectrum signal generated at each of the plurality of times from each of the plurality of receivers, wherein the processing circuitry is configured to process the indicator data from the plurality of lighting elements to determine an occupancy count in the area at each of the plurality of times.
 29. The system of claim 28 wherein: one or more of the plurality of wireless transmitters is one of a WiFi, blue tooth low energy, Zigbee, nLightAir or an ultra-wide band transmitter; and one or more of the plurality of receivers is one of a WiFi, blue tooth low energy, Zigbee, nLightAir or an ultra-wide band receiver.
 30. The system of claim 28 further comprising a controller coupled to the processing circuitry to control the light source in response to determination of the occupancy count in the area at each of the plurality of times.
 31. The system of claim 28 wherein to process the indicator data from the plurality of receivers to determine the occupancy count in the area at each of the plurality of times, the processing circuitry at each respective one of the plurality of times is configured to: apply a RF signal computation to analyze the indicator data of the RF spectrum signals from each of the plurality of receivers; compare the analyzed indicator data with a pre-determined number of the occupants in the area; and determine the pre-determined number of occupants to be the occupancy count in the area based on a result of the comparison.
 32. The system of claim 28 wherein the received RF spectrum signal is a line of sight (LOS) signal and/or a multipath signal.
 33. The system of claim 28 wherein to process the indicator data from the plurality of receivers to determine the occupancy count in the area at each of the plurality of times, the processing circuitry at each respective one of the plurality of times is configured to: apply a heuristic algorithm to the indicator data of the RF spectrum signals from each of the plurality of receivers to compute an output metric value for each of a plurality of probable number of occupants in the area; compare each one of the plurality of occupant values with another one of the plurality of output metric values to determine one of the plurality of computed output metric values as having a largest value among the plurality of computed output metric values; and determine the probable number of occupants associated with the computed output metric value having the largest value as the occupancy count in the area.
 34. The system of claim 28 wherein the processing circuitry to determine an occupancy count in a sub-area within the area.
 35. The system of claim 34 wherein the processing circuitry to: reject the indicator data of RF spectrum signals generated by a receiver among the plurality of receivers located outside of the sub-area and within the area; and reject the indicator data generated by a receiver among the plurality of receivers of the RF spectrum signals received from a transmitter among the plurality of transmitters located outside of the sub-area and within the area. 